There’s one question I keep getting everywhere.

“Will AI replace us?”

And honestly… I get it. Especially if you’re still learning, this question can sit in your head all day.

Now if you ask me directly, my answer is a bit annoying.

It depends.

I know… not the answer you want. But I’m not trying to avoid it. The problem is, this question is not simple, and most of the discussions online are missing something very important.

They ignore how things actually work inside real companies. So instead of giving you a short answer, let me walk you through how I think about this.

The reality inside companies

What you see online vs what actually exists

Most AI demos you see are built on very clean setups. A few tables, nice structure, everything documented perfectly. In that world, AI looks insanely powerful.

But real projects don’t look like that at all. They’re messy, huge, and everything is connected to everything.

I worked on a lakehouse project where we spent years just connecting systems. Data was coming from APIs, Kafka streams, databases, files… and we ended up with more than a thousand tables. And honestly, that was just the starting point. On top of that, there was business logic built layer by layer over time.

So when someone says “AI can build this with a few prompts”… yeah, it sounds nice.
But it skips the hardest part.

The hard part is not writing code.
It’s understanding everything behind the code.

Companies move slower than technology

Another thing people underestimate.
Technology moves fast. Companies don’t.
If you watch what’s happening in the media, it feels like everything is already AI-driven. Like every company has figured it out and we’re already there.

But the moment you step inside a real company, you see a completely different reality.
You still find Excel running critical reports. You see systems that have been there for years and no one wants to touch them. And there’s always a big chunk of data that isn’t even integrated yet.

And changing all of this… it’s not quick.

You need to migrate systems, build pipelines, set up governance and security, and at the same time train people so they actually understand what’s going on. And all of this has to move together.

So when I hear someone saying that jobs will disappear very soon, I usually just pause for a second and think… yeah, let’s see 🙂

Not because AI is weak. But because companies don’t change overnight.

How AI changes data teams

Now let’s talk about data teams.
Most companies have a lot of data, but small teams. So they only process a small portion of it.
Then AI comes in and makes those teams faster. And this creates a real decision.

Do you reduce the team, keep the team, or expand?

If you reduce the team, you save cost… but nothing really improves. You’re still processing the same small portion of data, and you might even hurt quality because the team was already stretched.

If you keep the team, you start doing more. More data sources, more pipelines, more insights. This is what I see most often. It works well, but only to a certain point. Eventually, you hit a new limit.

If you expand the team, you treat AI as a growth opportunity. You invest more, improve data quality, and use a much bigger portion of your data. It costs more, but the value is much higher.

And from what I’ve seen… most companies are not rushing to reduce data teams. They either scale what they already have or start investing more.

Because in the end, AI doesn’t reduce the need for data work.
It actually increases it.

What I think about the job roles

Instead of asking “Will AI replace data analysts or engineers?”, I prefer to break the job into parts. Because not everything is equal.
Let’s start with data analysts.

Will AI Replace Data Analysts?

SQL
is the first thing everyone talks about. And yes, AI is already very good here. You describe what you want, and it gives you a query. For simple things, it works great.
But once things get messy, multiple joins, unclear schemas, relationships… you start to see the limits. It doesn’t really understand your data, it just predicts.
Still, if I’m honest, this part will mostly be automated.

Dashboards:
AI can generate charts, but real dashboards are not just visuals. They include business logic, multiple datasets, filters, edge cases… and most importantly, trust.
At the end, someone still needs to look at the numbers and say, “this is correct.”
Then there’s something people don’t talk about enough.

Finding the right data:
In real companies, this is not obvious at all. You might have multiple tables that look the same, different systems storing similar data, and no clear documentation.
So what do you rely on? Experience. Context. Conversations.
This is where analysts actually spend a lot of their time, and this is where AI struggles the most.

Understanding the business:
Not just what people ask… but what they actually mean. Sitting with stakeholders, asking better questions, translating vague ideas into something measurable.
This is not about tools anymore. This is about thinking.
And here, AI is not even close.

Now to be fair, there is one part that will disappear.
The repetitive stuff. The small requests, the same questions again and again, the quick checks.
And honestly… that’s a good thing.
So when people ask me if AI will replace data analysts, my answer is always the same.
If your work is mostly writing queries and building simple dashboards, then yes, a big part can be automated.
But if you understand the business, work closely with people, and build things others rely on, then you’re not being replaced.

Will AI Replace Data Engineers?

Now let’s look at data engineers.

Coding:
AI is already very strong here. It can generate SQL, Python, and Spark code quickly. And this will only improve.

Build data pipelines:
AI can already create simple ones, and in the future, it will likely handle much more. Still, someone needs to review before anything goes to production.

Architecture:
Designing how systems are structured. Choosing between lakehouse or warehouse. Defining layers. Handling performance and scalability.
This requires deep understanding of both the technical side and the business context. It’s not something you can fully automate.

Data Modeling:
This is one of the hardest parts. You need to understand how different data sources connect, how data will be used, and which modeling approach makes sense.
Even experienced engineers find this challenging.
I don’t see AI replacing this anytime soon.

Operations:
Operational tasks like monitoring pipelines can be automated. AI can detect issues, suggest fixes, and possibly resolve them.

So again, the same pattern appears.
If someone is only coding and building pipelines, their work can be automated.
But if they understand systems, architecture, and data modeling, they become extremely valuable.

The role of data in the AI world

There is one important idea that didn’t change.

Before AI, we already knew:

Bad data leads to bad analysis.

Now with AI, it’s exactly the same.

Bad data leads to bad results.

So someone still needs to prepare that data properly. And this is why data roles are not disappearing. They are evolving.

So …

AI is not replacing jobs in a simple way.
It’s replacing parts of jobs.

• The repetitive parts.

• The mechanical parts.

• The parts that don’t require deep thinking.

But the more you move toward understanding, decision-making, and communication, the harder it becomes to replace you.

If you’re thinking about your future, don’t try to compete with AI. Work with it.
Learn how it fits into your field. Use it to move faster, but don’t let it replace your understanding.

For me, AI is not something to fear. It’s something that removes the boring parts of the job and lets you focus on what actually matters.

And over time, you’ll start to see clearly what is real and what is just noise.
If you’re still here, I appreciate it.

Much love,
— Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

Today we’re going to do something a bit different.

Instead of talking about how to become a data engineer, I want to focus on the mistakes that might be stopping you from getting hired.

Over the last few years, I’ve seen many people move into data engineering. They learn Python, SQL, build projects, follow roadmaps… and still don’t land a job.

Yes, AI made things more competitive.

But companies are still hiring data engineers, even juniors. I see it from your messages, LinkedIn, and inside teams.

So the real question is not whether it’s possible.

“Why do some people succeed while others stay stuck?”

After years in the industry, I can tell you it’s not random. The same mistakes keep repeating.

So in this article, I’ll walk you through the biggest ones, and if you avoid them, things become much easier.

Mistake 1: Trying to Learn Every Tool in the Industry

Trying to learn every tool in the industry. I see this all the time.

Someone decides to become a data engineer and makes a big list of tools to learn.

Kafka, Airflow, Spark, Snowflake, Databricks, AWS, Azure, dbt…

At the end, the roadmap looks like a technology supermarket.

This is a mistake.

If you try to learn everything at the same time, you end up knowing a little about everything, but nothing deeply.

And companies don’t hire you for that. They hire you based on your project experience.

In real projects, you don’t use all those tools. You use a specific stack, like SQL, Python, Spark, Databricks.

And in interviews, they go deep. They expect you to understand the architecture, the components, and how things work.

“If you try to learn everything, you end up mastering nothing.”

So instead of learning 20 tools on the surface, focus on one stack and go deep.

Mistake 2: Focusing Only on Tools

Focusing only on tools and forgetting about the real data engineering skills.

I can tell you from my own experience. Back in 2013, I built my first data warehouse using Talend, then moved to IBM DataStage. Later at Mercedes-Benz, I worked with Azure Data Factory, Synapse, and eventually Databricks.

Different tools, same problems.

That’s the key.

Tools keep changing, but the core concepts stay the same.

If you only learn tools, it becomes very hard to move from one tool to another.

That’s why you need to spend some time understanding the fundamentals.

Things like:

• What is a data warehouse, data lake, lakehouse

• ETL vs ELT

• Batch vs streaming

• Data modeling (star schema, snowflake, Data Vault)

You don’t need to master everything, but you should understand what they are and when to use them.

Because companies ask about this.

“Tools change, but concepts stay.”

So yes, learn tools, but also take time to understand how data engineering actually works.

Mistake 3: Ignoring SQL

Ignoring SQL. This one really surprises me.

I’ve seen many engineers who are good with Python and Spark, but they struggle with very simple SQL queries, even basic joins.

A lot of people jump directly into big data tools because they want to work on the “cool” stuff.

But they skip one of the most important skills.

SQL.

And sometimes I hear things like: SQL is dead, it’s not cool, it’s only for beginners.

But this is not true.

In real projects, a huge part of your work still happens in SQL.

Because almost every platform like Databricks, Snowflake, and even tools around Kafka, allow you to use SQL to work with data.

You will use SQL to:

• explore data

• debug issues

• build transformations

• apply business logic

For example, in the Silver layer, you might use PySpark, but very often you also use SQL.

In the Gold layer, you will use SQL heavily for business rules.

“If your SQL is weak, your daily work becomes harder.”

So don’t skip SQL. Start with it, get comfortable, then move to Python and Spark.

Mistake 4: Building Toy Projects

Building toy projects.

Projects are very important today. Sometimes they matter more than certificates because they show what you can actually build.

But most of the projects I see are very small. If you look closely, many of them are just loading one CSV file, cleaning it, and exporting another CSV.

Yes, technically it’s a pipeline, but this is not how real data engineering projects look.

In real scenarios, you work with multiple data sources. Data can come from APIs, databases, files, or even streams. And your job is not only to move data from A to B, but also to handle errors, manage retries, validate data quality, and monitor what is happening in the pipeline.

You also need to show how you think in terms of architecture. For example, building a lakehouse with Bronze, Silver, and Gold layers, and explaining the role of each one.

You can also make your project more realistic by adding bad data, testing with larger datasets, and checking how your pipeline performs under pressure.

“Small projects show effort, but real projects show how you think.”

So don’t build many small projects. Build one serious project that looks like a real system.

Mistake 5: Learning Outdated Technologies

Learning outdated technologies.

I’ve seen people follow roadmaps or courses that are already 8 to 10 years old. They spend a lot of time learning things like managing Hadoop clusters, manually configuring Spark, or working heavily with on-premise ETL tools.

Now don’t get me wrong, these things were very important in the past.

But the data ecosystem has changed a lot.

Today, many companies don’t want engineers spending their time managing infrastructure. They want them to focus on building pipelines, working with data, and delivering value.

That’s why many teams are using platforms like Databricks or Snowflake, where most of the infrastructure is handled for you.

So instead of spending months going deep into cluster management and legacy systems, it’s better to focus on how modern data platforms work and how to build real solutions on top of them.

“If you prepare for the past, you might miss where the industry is going.”

Of course, it’s good to understand the basics of older systems, but don’t go too deep into them. Focus on the direction the industry is moving today.

Mistake 6: Going to Extremes with AI

Going to extremes with AI.

Right now, I see two types of people trying to become data engineers.

The first group relies completely on AI. They open a tool, generate a data pipeline, copy the code, run it, and move on without really understanding what is happening.

This becomes a problem very quickly.

In real projects, things are more complex. You deal with multiple data sources, messy data, and unexpected issues. And when something breaks, you need to understand what is going on to fix it.

The same thing happens in interviews. If someone asks you why you designed something in a certain way or how you would debug a failure, it becomes difficult to answer if you were only copying code.

On the other side, there are people who completely ignore AI. They don’t use it at all, don’t know how to write prompts, and don’t take advantage of it.

This is also a mistake.

Today, companies expect engineers to know how to use AI to speed up their work, whether it’s debugging, generating ideas, or improving solutions.

“AI should support your thinking, not replace it.”

So the goal is balance. Use AI as a tool to help you, but make sure you understand what you are building.

So …

These are the mistakes I keep seeing again and again from people who feel stuck and struggle to get hired.

The good part is, none of them are permanent. You can fix all of them.

You don’t need more tools, and you don’t need more random courses.

You need to focus, go deeper, and actually understand what you are building.

If you fix these things, you’ll already be ahead of many others.

“Getting hired is not about doing more, it’s about doing the right things well.”

And if you’re currently searching for a job, I really hope you land one soon.

You’re probably closer than you think.

The first video is already out, and many more are coming.

Much love,
— Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

I just published the first video in a new series where I’ll be sharing everything I know about Databricks!! 🎉

For the past 5 years, I’ve been building data projects almost entirely on one platform: Databricks. And honestly, it worked like magic.

So now I want to share everything I’ve learned from those years of building real projects.

But if you know my style, you know I don’t like jumping straight into a tool or platform… I like to start with the foundations.

The big questions: What is it? Why does it exist? Who is it actually for?

The interesting thing is that this platform is not only for data engineers anymore.

Today it’s used by data analysts, data scientists, ML engineers, and even AI engineers.

let’s start with the story.

The Single Machine Era

In the beginning, everything ran on a single machine.

Companies stored their data on one server and analysts or engineers ran SQL queries or Python scripts to analyze it. For small datasets this worked perfectly. The machine had enough CPU, memory, and storage to process everything.

But as companies digitized more of their business, data volumes exploded. Websites started generating event logs. Applications produced telemetry. Businesses began storing historical records for years.

Suddenly data grew from gigabytes to terabytes.

One machine was no longer enough. Queries took hours. Systems crashed under heavy workloads. And if that single machine failed, the entire analytics process stopped.

One computer works for small data, but it collapses when data becomes massive.

The Hadoop Era

To solve this scaling problem, Hadoop introduced distributed computing.

Instead of relying on one powerful computer, Hadoop used many smaller machines working together in a cluster. Data was split across those machines, and each machine processed its piece in parallel.

This approach allowed companies to process massive datasets that were impossible to handle before.

But Hadoop had a big drawback.

Every computation required reading data from disk, processing it, and writing it back to disk again.

Disk operations are slow compared to memory. As datasets grew larger, Hadoop jobs could take hours or even days to complete.

Hadoop solved the scaling problem, but processing data from disk made everything slow.

The Spark Era

Spark was created to solve the speed problem.

It kept the distributed system idea from Hadoop but changed how computations happened.

Instead of constantly reading and writing to disk, Spark processed data in memory whenever possible.

Memory operations are dramatically faster than disk operations. This made Spark many times faster for most workloads.

Companies quickly adopted Spark as the new standard for big data processing.

But there was another issue.

Early Spark development required knowledge of Scala or Java, which limited who could use it. Most analysts and data scientists were not comfortable with those languages.

Spark made big data fast, but it was still difficult for many data professionals to use.

The PySpark Era

At the same time, Python was becoming the dominant language for data science.

Libraries like Pandas, NumPy, and Scikit-learn made Python extremely popular for data analysis and machine learning.

To connect these worlds, PySpark was introduced.

PySpark allowed developers to write Spark programs using Python while still benefiting from distributed computing.

This opened big data processing to a much larger audience.

However, running Spark at scale was still complicated. Engineers had to configure clusters, manage resources, monitor failures, and handle infrastructure issues.

Before even writing data pipelines, teams often spent weeks setting up the environment.

PySpark made big data accessible, but managing the infrastructure remained painful.

Databricks as a Big Data (Managed Spark) Tool

To remove this operational burden, the creators of Spark founded Databricks.

The idea was to provide Spark as a fully managed platform.

Instead of configuring clusters manually, teams could run workloads on a platform where infrastructure, scaling, and environment setup were handled automatically.

This allowed engineers and analysts to focus on building data pipelines, running analysis, and developing models.

But a new problem appeared.

Because storage became cheap and easy, companies began dumping huge amounts of raw data into data lakes.

Over time, many of these lakes became messy. Schemas changed, data quality dropped, and teams lost trust in the datasets.

People started calling them data swamps.

Managed platforms simplified infrastructure, but uncontrolled data lakes created chaos.

Databricks as Data Platform ( Data Lakehouse & SQL)

To fix the chaos of data lakes, Databricks introduced the Lakehouse architecture.

The goal was to combine the best parts of two worlds:

Data lakes for scalability and flexibility.
Data warehouses for reliability and structured analytics.

Using technologies like Delta Lake, data lakes gained important capabilities such as transactions, schema enforcement, and versioning.

At the same time, Databricks introduced powerful SQL capabilities, allowing analysts to query the same data layer using familiar tools.

This meant engineers, analysts, and data scientists could all collaborate on the same platform and the same data.

However, as organizations grew larger, another challenge emerged. Data governance.

The Lakehouse unified the data stack, but large organizations needed strong governance.

The Unity Catalog Era

In enterprise environments, managing data access is critical.

Companies must control who can see which datasets, track how data flows across systems, and ensure compliance with regulations.

Without governance, data platforms quickly become risky and unmanageable.

To address this, Databricks introduced Unity Catalog.

Unity Catalog centralizes governance across the platform. It manages catalogs, schemas, tables, permissions, and lineage in one place.

This allows organizations to maintain security, transparency, and compliance while still enabling collaboration.

As data platforms scale across organizations, governance becomes essential.

Databricks as Data & AI platform

The industry is now shifting again, this time because of AI.

Companies want to build intelligent systems using their data. But in many organizations a new problem appeared.

The data lived in the data platform, while AI engineers were building systems somewhere else.

To create AI applications, teams had to build separate infrastructure for model training, LLM integration, and deployment. This created another fragmented architecture.

To solve this, Databricks extended the platform with Mosaic AI.

Mosaic AI allows teams to build, train, and deploy AI systems directly inside the platform, using the same data and infrastructure.

This means data pipelines, analytics, machine learning, and AI applications can now live in one place.

Databricks evolved once again.

Not just into a data platform, but into a Data + AI platform.

The future of data platforms is bringing AI development directly where the data lives.

So …

If we step back and look at this entire journey, we can see something interesting.

Every generation of technology solved one major problem.

• Single machines worked for small datasets.

• Hadoop made it possible to scale to massive data.

• Spark made big data processing fast.

• PySpark opened the door for Python users.

• Databricks removed the complexity of managing infrastructure.

• The Lakehouse unified data lakes and data warehouses.

• Unity Catalog introduced governance for enterprise environments.

• And today the platform continues evolving into a Data + AI platform.

Databricks is a unified platform where data engineering, analytics, machine learning, and AI development happen in one place.

If you want to understand how modern data platforms actually work, join me on YouTube. I’ll be sharing much more content like this in the coming weeks.

The first video is already out, and many more are coming.

Much love,
— Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

The waiting is finally over. I’ve waited more than a year to write these words… and today is the day.

🎉 My full 13-hour Python course, taking you from absolute beginner to confident Python developer, is officially LIVE! 🎉

Let’s go.

Why You Should Learn Python in 2026?

Look around at what is shaping technology today.

AI assistants. Data platforms. Smart automation. Cloud systems.
Behind almost all of it, there is Python.

It has quietly become the language that powers modern innovation.

If you want to step into AI, data engineering, analytics, backend development, or simply become more efficient in your current job, Python opens that door. It connects tools, systems, and ideas together.

And if you are new to coding, you might think this world is not for you. But here is the truth: Python was designed to be readable, logical, and approachable. You do not need a computer science degree to start. You just need curiosity and consistency.

For data analysts especially, Python is a game changer. It transforms you from someone who works inside tools… to someone who can build solutions beyond them.

Python is not just a skill. It is a language of leverage.

Quick Guide to Content

1️⃣ Free The Core Content

👉 Watch on YouTube
The complete 13-hour Python course, available for free:

• All lessons and chapters

• All built-in challenges

2️⃣ The Handbook & Code Bundle

👉 Get the Handbook
A structured reference system that supports the full course:

• Complete Python Handbook (PDF)

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3️⃣ Data Academy: The Full Package

👉 Enroll in the Full Program
A guided learning platform that includes everything in one structured system:

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If you are a student with financial limitations and want access to the full program with certificate, comment “Python” under my LinkedIn post and I will send you Free Access Link (Limited for 100 Enrollments)

A Year in the Making

For more than a year, I worked on this course during weekends, late nights, and early mornings next to my full time job. It was not rushed. It was built slowly and intentionally, with one clear goal in mind

.

Not just to teach Python, but to help you think in Python.

I didn’t want this to be just another screen recording where you watch someone type for hours and try to keep up. That kind of learning feels productive in the moment, but a week later most of it fades.

If you know me, you know that’s not my style.

I wanted you to understand what really happens behind the scenes. What a variable actually is. How memory works. Why a loop behaves the way it does. What truly happens when you call a function.

That is why I hand drew every single visual myself on my iPad using Procreate. Every diagram, every flow, every explanation was created manually. I broke down each concept into simple visual animations, because when you can see the logic, it becomes clear.

And once you see it clearly, you do not just write code. You write it with confidence and understanding.

What’s Inside the Course?

Link to Mindmap

1️⃣ Python Foundations

• What Python is and how it works
• Installation and setup
• Comments and output
• Variables and user input
• Core data types

2️⃣ Working with Strings

• String fundamentals and transformations
• Indexing and slicing
• Searching, validation, and formatting

3️⃣ Working with Numbers

• Integers and floats
• Mathematical operations
• Rounding, randomness, and validation

4️⃣ Logic and Operators

• Boolean values
• Comparison and logical operators
• Membership and identity

5️⃣ Conditional Statements

• If, elif, else
• Nested conditions
• Inline if and match case

6️⃣ Loops

• For loops
• While loops
• Break, continue, pass
• Nested loops and for-else

7️⃣ Data Structures

• Lists with deep dive into tools.
• Tuples
• Sets
• Dictionaries

8️⃣ Functions

• Writing and structuring functions
• Parameters and return values
• Different types of functions
• Clean and reusable design

❤️ If You Appreciate It, Help Me Spread It

This course is free. No ads. No sponsors. Just me, sharing my knowledge
So if you find it helpful, please consider helping me spread the word:

👍 Like
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📩 Send it to that one person who needs to learn Python
📺 Subscribe on YouTube

It helps the YouTube algorithm show this content to other amazing learners like you.

Thanks for being part of this journey.
Now go dive in and explore Python like never before.
Let’s make learning fun again 🚀

Much love,
— Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

Over the years, life taught me lessons the hard way about time, work, money, relationships, and myself. This post is not advice or a formula to follow. These are simply the rules I live by today, shaped by experience and mistakes.

Take what resonates with you. Leave the rest.

Rule 01 – Everything Takes Time

Nothing real grows fast:

• Your body changes slowly
• Your skills compound slowly
• Your money grows slowly

Modern life sells speed. Fast results. Instant change. Those shortcuts always charge interest later.

Time will pass anyway. Build things that last.

“The long path is the real shortcut.”

Rule 02 – It Is Never Too Late

Being “too late” is usually just fear in disguise.

I’ve seen capable people stop themselves because they compare timelines and assume they missed their chance. Time doesn’t care. It keeps moving whether you act or not.

Every meaningful thing in my life started later than I thought it should. Starting anyway changed everything.

It is never too late. Time will move forward with or without you.

Rule 03 – Don’t Talk About Your Plans

Talking creates a fake sense of progress.

When you explain your plans in detail, your brain feels rewarded before the work is done. Encouragement replaces execution. Energy leaks into words instead of action.

Share direction if you must. Keep details to yourself. Work quietly.

“Execute first, Show results later, Celebrate after!”

Rule 04 – Don’t Compare Yourself With Others

Comparison never tells the full story.

Everyone starts from a different place. Different backgrounds, support, timing, and luck. Timelines are not comparable.

You only see the wins. You don’t see the sacrifices, the struggles, or the failures behind them. That’s why copying someone else’s path never works.

“Run your own race. Comparison leads nowhere”

Rule 05 – My Life. My Rules

Everyone has an opinion about how you should live. Family. Culture. Social media. The system.

If you let those voices decide for you, you slowly lose control.

I listen to advice. I think about it. But I keep the final decision. This is my life, and I live with the consequences.

“Live your own life, or you will end up living someone else’s.”

Rule 06 – Take Ownership of Your Life

Blame feels comforting, but it keeps you powerless.

Life is unfair. Complaining and blaming only focus your energy on what you can’t control.

Change the question. Stop asking “Why is this happening to me?” and start asking
“What can I do about it?” There is almost always something.

Ownership gives you control back.

“Take ownership, or stay stuck.”

Rule 07 – Be Careful What You Consume

Most content is designed to trigger you.

Fear. Urgency. Anxiety. Different platforms, same game. Click now. You’re late. You’ll regret it.

News sells fear. Social media sells perfect lives. After a while, you start thinking something is wrong with you.

Nothing is wrong with you. Real life is quieter, slower, and more normal.

“Protect your mind. Most content is designed to trigger, not to help.”

Rule 08 – No One Is Coming to Save You

When life gets hard, it’s easy to wait. For help. For luck. For someone to fix things.

Support may come, but no one can fix your life for you. That responsibility never leaves your hands.

Waiting keeps you stuck. Showing up for yourself moves you forward.

“Only you can fix your life.”

Rule 09 – Protect Your Relationship

Your partner is often the longest relationship in your life.

• Parents grow older and leave us.

• Children grow up and move on.

• Friends get busy with work and family.

A relationship is not a competition. There is no winning an argument. If one person loses, both lose.

Protect it, especially when things get hard.

“Protect your relationship. It is the longest one you will ever have.”

Rule 10 – Forgive to Move On

This one is hard.

Forgive your parents. They are not perfect. They made mistakes, often without bad intentions. They come from a different generation, with different tools. You cannot change that anymore. Forgiveness frees your energy.

Stay in touch. Call them. A short call is enough. Time moves faster than you think, and one day you will wish you had more of it.

Forgive yourself too. You will make mistakes. Learn, adjust, and move forward.

“Forgiveness makes you lighter. Lighter people move faster.”

Rule 11 – Review & Redesign Your Habits

Once a year, I take time to redesign my life.

I sit down alone, without distractions, and draw a simple timeline with three versions of myself: my younger self, my current self, and my future self.

I ask:

• “What did I do well? What mistakes did I make?”

• “Who do I want to become next?”

To answer that, I use a simple framework with five columns:

• Keep – habits that already work
• Start – habits I want to add
• Stop – habits that hurt me
• More – good habits I want more of
• Less – things I need to reduce

“Small habit changes, reviewed regularly, shape a better version of you each year..”

Rule 12 – Daily 30-Minute Walks

Walking clears my mind better than anything else.

Almost every day, I walk for 30 minutes. No phone. No music. No podcasts. Just walking.

Big decisions become smaller when you walk. Problems simplify. Sleep improves. Clarity follows.

It costs nothing, but over time, it changes everything.

“It costs nothing! Walk daily. Clarity follows.”

Rule 13 – Divide Your Day Based on Energy

I don’t manage my time. I manage my energy.

I split my day into three phases.

• Mornings are for deep work. This is when my energy is highest. Writing, planning, solving hard problems. I protect this time.

• Midday is for communication. Meetings, emails, conversations. Useful work, but not deep work.

• Evenings are for life. Family, movement, rest. No big decisions. No hard problems.

I am not productive all day, and that’s fine. I am productive when it matters.

“Understand your energy, then plan your day around it.”

Rule 14 – Three Levels of Learning

Learning something new happens in three levels.

I split my day into three phases.

Level 1: Consuming
You watch videos, read books, listen to podcasts. You understand the idea, but watching someone else do something does not mean you can do it. This level only opens the door.

Level 2: Practicing
This is where learning becomes real. You try, you get stuck, you make mistakes, and you move slowly. The struggle is not a problem. It is how knowledge is built in your brain.

Level 3: Teaching
This is the hidden level. When you explain something to others, you discover what you don’t fully understand. Teaching forces clarity, structure, and fills the gaps.

“You don’t really learn until you practice, and you don’t fully understand until you teach.”

Rule 15 – Learn Financial Literacy

I never understood why the most important life skill, how money works, is not taught in school.

Financial literacy is not about getting rich fast. It is about avoiding expensive mistakes and protecting yourself. Without it, decisions are based on guessing and trends, and that usually ends in loss.

A few simple rules I follow:

• Live below your means

• If you can’t explain where your money goes, you’re not in control

• Always keep an emergency buffer

• Don’t rely on one income source

• Get-rich-fast is a scam

• Don’t try to time the market

“Learn how money works before money teaches you the hard way.”

Rule 16 – Stability First, Freedom Later

This is the money model that worked for me.

Step 1: Get a stable job.
Nothing fancy. Work, get paid. This phase gives stability and a strong base.

Step 2: Invest what’s left.
Even a small amount. Over time, money and time work quietly in the background.

Most people stop here, and that’s okay. But you still depend on one job.

Step 3: Build side projects.
Something you enjoy. Something that takes time. It may fail, and that’s fine. You gain skills. Or it grows and creates freedom. That’s what happened to me.

“Stability first. Freedom later. No shortcuts.”

Rule 17 – Play the Long Game

I invest simply and consistently.

No daily tracking. No news watching. No stress. I invest regularly and leave it alone.

Yes, some people got rich fast with things like Bitcoin. Many more lost money just as fast, and those stories rarely make it to the media. That’s not my style.

I don’t chase luck. I build slowly.

“Simple, consistent, and long-term beats stress and guessing.”

Rule 18 – Complaining Is a Career Killer

Every job has problems. That’s normal. The mistake is dealing with them by constantly complaining.

Repeated complaining drains the energy of motivated people and adds weight to those who are already struggling. Over time, you get labeled as negative, people avoid you, and you slowly damage the team culture.

There are only three healthy ways to deal with problems at work:

• Ignore it if it’s out of your control
• Fix it by bringing a solution, not just a complaint
• Leave if it truly matters and nothing changes

“Fix it, ignore it, or leave. Complaining kills careers..”

Rule 19 – Know When to Stay and When to Leave

If your ideas are always ignored and nothing changes, it’s time to leave.

I don’t leave just because work gets hard. Pressure and disagreements exist everywhere. But I never stay where there is no respect. Once an environment becomes toxic, that is a clear signal.

The other danger is staying too long. Comfort can stop growth. When learning stops, risk starts.

My rules:
• Don’t leave because it’s hard
• Don’t stay without respect
• Don’t stay too long without learning

Rule 20 – Don’t Burn Out. Don’t Check Out

At work, I’ve seen two extremes:

The Burnout Trap
Long hours. No rest. No life outside work. Health and relationships get sacrificed with the hope that the job will take care of you. Most of the time, it doesn’t.

The Checked-Out Trap
Doing the bare minimum. Avoiding responsibility. Waiting for the day to end. Work already takes part of your life. Hiding from it gives nothing back.

Both extremes lead to the same place.

“Give your best without burning out. Stay engaged without losing your life.”

Rule 21 – Colleagues Are Not Friends

People at work can be friendly, but work is still work. When you forget that, you overshare, and it often comes back to hurt you.

“Be kind. Be respectful. Be professional.”

Rule 22 – Communicate Your Work

I’ve seen many great people do great work that no one knows about. Not because it isn’t valuable, but because no one is tracking it.

People are busy. If you stay silent, your value stays invisible.

If you’ve built something useful, share it. Write about it. Present it to your team. Talk about it. This is not bragging. Someone out there needs what you built and doesn’t know it exists.

Clear communication helps others, and it helps you. Your work gets recognized, your impact becomes visible, and opportunities follow.

“Good work unseen is good work wasted.”

So my friends,

These are the rules I live by today. I’m somewhere in the middle of my life, and I know I’ll still make mistakes. But I’m committed to learning from them.

If I had to reduce everything to a few foundations, it would be this:

• I play the long game. I don’t rush.

• I don’t take things personally, because most things aren’t about me.

• I don’t live my life based on other people’s expectations.

• I never lose sight of what matters most.

• My health. My family. Everything else comes after.

You don’t have to follow my rules. We all walk different paths and face different realities. Take what resonates with you. Leave the rest.

If this helped you pause, reflect, or adjust even one small thing, then it served its purpose. I wish you peace, clarity, and a life you’re proud to look back on.

Have a wonderful day ❤️
Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

I just wanted to take a moment to say thank you for being a member and for supporting my work. It truly means a lot to me.

Your support, messages, and encouragement mean more than you know. This newsletter started as a simple way to share knowledge, and now it has become a community of motivated people who genuinely want to grow in dat…

Read more

You’re invited to join our 𝗗𝗮𝘁𝗮 𝗟𝗶𝘃𝗲 𝗦𝘁𝗿𝗲𝗮𝗺 𝘄𝗶𝘁𝗵 𝗺𝗲 & 𝗦𝗶𝗺𝗼𝗻!

We worked together for over three years on a real enterprise data project at Mercedes-Benz.

I’ve noticed that many real experts are actually hidden! Most of them simply don’t have the time to be active on social media or share their knowledge publicly.
That’s exactly why I want to bring these gems into the spotlight!

We’re having an open, relaxed conversation about real data projects, and lessons learned.
Join us live and drop your questions in the chat. We’ll answer as many as we can.

See you there 👋

Have a wonderful day ❤️
Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

I was looking at a bunch of “data analyst roadmaps” the other day, and I noticed something. Most of them are super tool-based. They basically say: learn SQL, Excel, Power BI, Python… and that’s it.

But tools are only one piece of the job. The bigger skill is learning how to think like an analyst: how to break down messy questions, understand the business, and turn data into decisions.

So I built my own roadmap. I’ll take you slowly through every phase, starting from total beginner, all the way to getting hired, then what to focus on after you get the job, and how to grow into senior and lead roles.

This roadmap is based on my 17 years working in real data teams, and what companies actually expect in the market today.

Here are the resources 👇

• I explained the full roadmap step by step in this video → LINK

• And I shared the Notion template with all phases, resources, and a clear step by step guide → LINK

#1 The Hiring Journey

This journey is about building the right skills to get you ready for your first data analyst role.

Phase 0 - Absolute Beginner 🧭

This phase is about understanding what data analytics really is and deciding whether this career is right for you.

🎯 Mindset: Mindset: Is data analytics the right career for me?

Just spend like few days reading watching about it and make then decision

• Understand the Role: You clearly understand what a data analyst actually does before investing time and effort.

• Check 10 Job Posting: Scan job postings to see what companies actually ask for and to align your learning with the market.

• Make Decision: Decide if data engineering is the right long-term career for you.

Remember: Ask yourself: “Would I enjoy this work every week?” Be honest with yourself. This phase saves you years later.

Phase 1 - Core Technical Skills 🌱

This phase is about getting you ready by focusing on core skills one by one, so working with data and code starts to feel natural before you even think about data engineering.

🎯 Mindset: I can work with Data

How to Learn: Learn Theory, Basics and Deep dive into Advanced Topics, Do Projects

• Data & Analytics Terminology: Learn core terms like metrics, KPIs, BI, and data quality.

• SQL: Explore, clean, and join data to answer real business questions.

• EDA Project: Analyze a real dataset to find patterns, trends, and issues.

• Power BI / Tableau: Build dashboards that clearly communicate insights.

• Dashboard Project: Create an end-to-end dashboard using real data to show your skills.

Remember: Practice more than you consume content

• Learn one skill at a time

• Focus on understanding, not memorizing, Always ask “why” before “how”

• Use AI only to explain concepts, not to write code for you

Phase 2 - Data Analytics Skills 🧠

This phase is about how analysts think, not which tools they use. You move beyond writing queries and dashboards and start focusing on reasoning, interpretation, and decision-making.

🎯 Mindset: I can think like an analyst!

How to Learn: Just read about it no need to deep dive

• Statistics: Understand distributions, variability, and relationships. Focus on meaning, not formulas.

• Communication (Storytelling): Turn analysis into clear insights non-technical people understand.

• Analytical Thinking and Problem Solving: Break vague questions into measurable problems and challenge assumptions.

• Data Modeling and Metrics Design: Build meaningful metrics that reflect real business logic, not vanity numbers.

Remember: Clear thinking beats fancy dashboards

• Your main task is to answer business questions, not only to write queries or build charts

• You work with non-technical people, so clarity matters more than complexity

• Numbers are useless without context, assumptions, and impact

Phase 3 - Stand Out Go Beyond the Basics 🧳

This phase is about differentiation, not requirements. You already have what you need to get hired. These skills help you stand out, work faster, and handle more complex data, but they are not required for a data analyst role.

🎯 Mindset: I add extra value without losing sight of the core role.

How to Learn it: Learn the core concepts first, then practice only the basics. Avoid deep dives. Depth comes later on the job.

🛑Important Note: These are large topics. Do not get lost in tools or delay job applications. This phase is optional and exists only to help you stand out, not to block your progress.

• Python (Pandas, Numpy, Plotly): Use Python to explore data and automate repetitive work.

• Databricks For Data Analytics: Learn how analysts explore large datasets on modern platforms.

• AI Prompt Engineering: Use AI to speed up analysis, SQL, and documentation.

• AI Models (High-Level): Understand what AI models do, their limits, and how analysts use them.

Phase 4 - Prepare & Get Hired 💼

At this point, you won’t know 100% of data engineering, and that’s completely normal. The good news is you already have around 70% of the skills needed to start applying. There’s no reason to wait anymore. Now it’s time to prepare yourself, put your profile together, and start applying for jobs.

🎯 Mindset: I am ready to enter the market!

• Get Certified: Validate skills with relevant certifications. You increase trust and visibility in the market.

• Build Clean Resume: Present skills and projects clearly. Recruiters quickly understand your value.

• Optimize LinkedIn Profile: Make your profile searchable and attractive. Recruiters can find and contact you.

• Build Portfolio: Showcase real projects publicly. You stand out beyond certificates and courses.

• Start Applying for Jobs: Apply consistently and interview actively. You gain interview experience and land offers.

• Improve & Learn: Fix gaps while applying and interviewing. You continuously improve until you get hired.

Remember: You don’t need 100% to start applying.

• Apply before you feel fully ready

• Rejections are part of the process

• Treat interviews as learning sessions

• Focus on explaining, not impressing

• Keep improving while applying

• Do not compare yourself to seniors

End of The Hiring Journey🎉

If you got a job, congratulations. I’m really proud of you and happy for you.

From here, I’ll guide you on how to grow as a data analyst and build real experience on the job.

If you’re still searching, don’t worry. You will get one. Just keep pushing.

In the meantime, you can already start looking at the next skills, and if you have time, pick a few to strengthen your profile while you keep applying.

#2 The Growth Journey

This journey is about what happens after you get hired: learning on the job, making mistakes, and growing into a strong data analyst.

Phase 5 - Junior Data Analyst 🧑‍💻

At this stage, you are officially working as a data analyst, but you will not know everything and that is completely normal. Most of your real learning happens on the job. You will ask questions, make mistakes, fix them, and slowly start connecting the dots.

🎯 Mindset: I am learning by doing

• Understand the business and stakeholders: Understand how the company makes money and who uses your work.

• Explore and Understand Data (EDA): Explore data, ask questions, and build intuition.

• Build Domain Knowledge: Learn the business domain to add context to your analysis

• Maintain and Improve Existing Reports: Maintain existing reports and improve clarity and metrics.

Remember: Making mistakes is part of the job.

• Ask questions early

• Focus on reliability over clever solutions

• Quality is always better than speed

• Learn company data and business context

• Take feedback seriously, not personally

Phase 6 - Senior/Lead Data Analyst 🧠

At this stage, you are no longer just executing tasks. You are trusted with complex business problems, ambiguous questions, and high-impact decisions. You influence stakeholders, shape metrics, and guide others.

A senior or lead data analyst focuses less on producing reports and more on driving clarity, alignment, and decisions across the business.

🎯 Mindset: I take ownership of complex problems and help others see the full picture.

• Mentor Juniors: Review work, give clear feedback, and help juniors improve their thinking.

• Impact Thinking: Move from “what was asked” to “what really matters.” Prioritize work based on impact, not volume.

• Improve data quality and governance: Ensure trusted, consistent metrics across teams.

• Advanced Data Modeling: Build scalable metrics for long-term decisions.

• System-Level Analytics Architecture: Understand how data flows across analytics systems.

Remember: Delegate simple tasks and focus on complex, high-impact problems.

• Let juniors handle repetitive analysis and reporting

• Own the hardest business questions

• Review work to raise the overall analytics quality

• Balance insight quality, speed, and trust

• Help stakeholders make better decisions, not just faster ones

So my friends,

You can start this path with no degree and no prior experience. I have seen it happen many times. The roadmap is simple. Learn the core skills, build real projects, create proof of your work, and keep improving while you apply. It takes time and patience, but it works.

If you want a clear structure to follow, you can use the full Notion Data Engineering Roadmap here → LINK
It includes all phases, resources, and guidance to help you move step by step.

Thank you for taking the time to read this. I hope it brought you clarity and helped you feel more confident about your next move.

Have a wonderful day ❤️
Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I’m Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

Last year, I shared a roadmap on how to become a data engineer. I honestly did not expect the impact it would have. Many of you followed it step by step, and some of you even told me you landed your first job.

So I went back and reviewed the entire roadmap. I updated it based on today’s market and extended it.

This time, it does not stop at getting hired. It shows what to learn after you join a company, from Junior to Senior, and eventually to Data Architect.

And Learning data engineering today can be very confusing! There are too many tools, too many skills.

So to clear the confusion:

• I explained the full roadmap step by step in this video → LINK

• And I shared the Notion template with all phases, resources, and a clear step by step guide → LINK

#1 The Hiring Journey

This journey is about building the right skills to get you ready for your first data engineering role.

Phase 0 - Absolute Beginner 🧭

This phase is about understanding what data engineering really is and deciding whether this career is right for you.

🎯 Mindset: Is data engineering the right career for me?

• Understand the Role: You clearly understand the job before investing time and effort.

• Make Decision: Decide if data engineering is the right long-term career for you.

Remember: Be honest with yourself. This phase saves you years later.

• Ignore hype and salary videos

• Ask yourself: “Would I enjoy this work every week?”

Phase 1 - The Foundations 🌱

This phase is about getting you ready by focusing on core skills one by one, so working with data and code starts to feel natural before you even think about data engineering.

🎯 Mindset: I am building my base!

• SQL: You can work with data using SQL

• Python: Write scripts to process, clean, and transform data.

• GitHub: Track changes and manage code professionally.

Remember: Practice more than you consume content

• Learn one skill at a time

• Focus on understanding, not memorizing

• Use AI only to explain concepts, not to write code for you

• Avoid tools and platforms for now

Phase 2 - Data Engineering Core ⚙️

In this phase, you start going deeper into data engineering. You’ll learn the core concepts and tools, and more importantly, you’ll start thinking like a data engineer, not just writing code.

🎯 Mindset: I am becoming a data engineer!

• DE Concepts & Terminology: Learn core ideas like pipelines, batch vs streaming, and data layers.

• Databricks: Use a modern platform to build and manage pipelines. You can work with industry tools used in real companies.

• PySpark: Process large datasets using distributed computing. You can handle data at scale, not just small files.

Remember: Concepts matter more than tools.

• Always ask “why” before “how”

• Learn how data flows end to end

• Do not rush through platforms

• It’s okay if things feel unclear at first

• Real understanding will deepen later on the job

Phase 3 - Prepare & Get Hired 💼

At this point, you won’t know 100% of data engineering, and that’s completely normal. The good news is you already have around 70% of the skills needed to start applying. There’s no reason to wait anymore. Now it’s time to prepare yourself, put your profile together, and start applying for jobs.

🎯 Mindset: I am ready to enter the market!

• Get Certified: Validate skills with relevant certifications. You increase trust and visibility in the market.

• Build Clean Resume: Present skills and projects clearly. Recruiters quickly understand your value.

• Optimize LinkedIn Profile: Make your profile searchable and attractive. Recruiters can find and contact you.

• Build Portfolio: Showcase real projects publicly. You stand out beyond certificates and courses.

• Start Applying for Jobs: Apply consistently and interview actively. You gain interview experience and land offers.

• Improve & Learn: Fix gaps while applying and interviewing. You continuously improve until you get hired.

Remember: You don’t need 100% to start applying.

• Apply before you feel fully ready

• Rejections are part of the process

• Treat interviews as learning sessions

• Focus on explaining, not impressing

• Keep improving while applying

• Do not compare yourself to seniors

End of The Hiring Journey🎉

If you got a job, congratulations. I’m really proud of you and happy for you.

From here, I’ll guide you on how to grow as a data engineer and build real experience on the job.

If you’re still searching, don’t worry. You will get one. Just keep pushing.

In the meantime, you can already start looking at the next skills, and if you have time, pick a few to strengthen your profile while you keep applying.

#2 The Growth Journey

This journey is about what happens after you get hired: learning on the job, making mistakes, and growing into a strong data engineer.

Phase 4 - Junior Data Engineer 🧑‍💻

At this stage, you’re officially working as a data engineer, but you still won’t know everything and that’s completely normal. Most of your real learning will happen on the job. You’ll make mistakes, ask questions, fix things, and slowly start connecting the dots. This phase is about turning daily work into experience and growing step by step into a confident data engineer.

🎯 Mindset: I am learning by doing

• Security Basics: Learn access control, privacy, and basic security practices. You avoid risky mistakes with data and systems.

• Apache Kafka: Work with real-time streaming data. You can build and maintain streaming pipelines.

• Cloud Azure & AWS: Use cloud services for storage and processing. You can deploy and run data systems in the cloud.

• AI Engineering: Connect data pipelines with ML and AI systems. You support modern AI-driven products.

Remember: Making mistakes is part of the job.

• Ask questions early

• Focus on reliability over clever solutions

• Quality is always better than speed

• Learn company data and business context

• Take feedback seriously, not personally

Phase 5 - Senior Data Engineer 🧠

At this stage, you’re no longer just executing tasks. You’re trusted with complex problems, bigger decisions, and guiding others. You start thinking about systems, performance, and long-term impact. Being a senior data engineer means not only delivering solutions, but helping the team move faster and smarter.

🎯 Mindset: I take ownership of complex problems and help others grow.

• Code Review: Review and improve others’ code. Code quality and team standards improve.

• Mentoring & Technical Leadership: Guide juniors and technical decisions. You multiply impact beyond your own tasks.

• Cost & Performance Optimization: Balance speed, reliability, and cost. Systems scale without wasting money.

• Advanced Data Modeling: Design scalable and analytics-ready models. Data becomes easier to use and trust.

• System Design & Architecture Thinking: Design complete data systems. You solve problems at system level, not task level.

Remember: Delegate simple tasks and focus on complex problems.

• Let juniors handle routine work

• Take ownership of the hardest pipelines

• Review code to raise the team level

• Think about performance, cost, and reliability

• Help others unblock faster

Phase 6 - Data Architect 🏛️

This phase is where you move from “how do we build it” to “how should this system be designed.” You focus on architecture, trade-offs, governance, and making decisions that shape the future of the data platform.

🎯 Mindset: I step back and see the whole picture.

• Decision Making & Influence: Make and defend long-term decisions. You influence direction, not just execution.

• Business & Technology Alignment: Align tech decisions with business needs. Your work directly supports company goals.

• Data Governance & Ownership: Define rules and ownership for data. Data becomes reliable, compliant, and trusted.

• Platform-Level Architecture: Design data platforms used by many teams. You shape long-term data strategy.

Remember: Your job is no longer writing code, it’s to discuss, decide, and design future systems.

• Think in systems, not individual pipelines

• Design how data flows end to end, not how one job runs

• Decide why a system exists before how to build it

• Optimize for long-term clarity, not short-term speed

• Write less code, but make better decisions

So my friends,

You can start this path with no degree and no prior experience. I have seen it happen many times. The roadmap is simple. Learn the core skills, build real projects, create proof of your work, and keep improving while you apply. It takes time and patience, but it works.

If you want a clear structure to follow, you can use the full Notion Data Engineering Roadmap here → LINK
It includes all phases, resources, and guidance to help you move step by step.

Thank you for taking the time to read this. I hope it brought you clarity and helped you feel more confident about your next move.

Have a wonderful day ❤️
Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

Hey, I'm Baraa, a Data Engineer with over 17 years experience, Ex-Mercedes Benz, where I led and built one of the biggest data platforms for analytics and AI.

Now I’m here to share it all through visually explained courses, real-world projects, and the skills that will get you hired. I’ve helped millions of students transform their careers.

Visit My Youtube Channel

Subscribe now

We’re just 4 Hours away away from Day 1 of Databricks Bootcamp at 16:00 CET (15:00 GMT).

Quick Links:

• Signup for Free → LINK

• Live YouTube Session Link → LINK

• Notion Roadmap & Tasks → LINK

• Discord Community (for bootcamp discussions and questions)→ LINK

• GitHub Repository → LINK

What we’ll do in the first day during the live session:

• Introduction to the Bootcamp (15 mins)

• Introduction to Databricks (1:30 h)

  • Introduction to Databricks (Theory)

  • Industry problems and why Databricks (Theory)

  • How Databricks is used in real company workflows (Theory)

  • How to get your free Databricks account (Demo)

  • Tour of the Databricks interface (Demo)

  • How to get your data into Databricks (Demo)

• Break (15 mins)

• How to Use Databricks as a Data Analyst (1:30 h)

  • Data analytics maturity (Theory)

  • Data analyst evolution (Theory)

  • How data analysts use Databricks (Theory)

  • Data exploration using the SQL editor (Project)

  • Building dashboards using Databricks (Project)

  • Chatting with your data using AI Genie (Project)

  • Using notebooks and PySpark as a data analyst (Project)

• Q&A and Recap

After the live session ends, you will receive an email from me with all the practice tasks. This is where the real learning happens. I really recommend you to do them.

See you very soon 🎉

Baraa

Databricks is becoming the go-to platform for many large companies to build their data strategy. I’ve seen this first-hand. I led one of the largest Databricks Lakehouse projects at Mercedes-Benz, where we delivered top-tier data products used by more than 40 analytics and AI teams. In this bootcamp, I’ll introduce Databricks from both perspectives, data analysts and data engineers, and show you how we actually use it in industry projects.

I decided to make this bootcamp completely free. To be honest, I see many bootcamps charging thousands, and I know that a lot of people simply cannot afford that. This bootcamp is for you. It’s built from real industry experience, not from documentation turned into slides.

Also want to be transparent with you. I have bigger plans around Databricks. I’m working on a full Databricks course for YouTube and the Data Academy, and I’m also planning a longer and deeper bootcamp over a full month for those who want a true deep dive. That extended bootcamp is still in planning and will be a paid.

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

The world of data is full of buzzwords.
If you’re learning data or preparing for interviews, it’s very easy to get confused or mix terms.

I didn’t learn data terminology in one go.
Over 17 years working with data, I picked it up step by step through real projects.

That’s why I created this cheat sheet.
It explains 200 core data terms in a simple, practical way and yes, I even made a YouTube movie about it 😄

A simple way to build data literacy: understanding data, speaking its language, and using it with confidence.

So before we jump into all these terms, let’s start with one simple question.

What is data literacy?

Data literacy is the ability to understand data, talk about it confidently, and make sense of it in real situations.
It’s knowing what data actually means, where it comes from, and how it’s used in real work.

Data Basics

Data Raw facts collected from systems, applications, and users before any processing.
Raw Data Data taken directly from source systems without any transformation.
Business Data Data prepared and structured to support business decisions. Structured Data Data organized in a fixed format of rows and columns.
Semi-Structured Data Data with partial structure using formats like JSON or XML.
Unstructured Data Data that does not follow a predefined structure.
Big Data Very large datasets that require specialized tools to process.
Data Volume The amount of data being generated or stored.
Data Velocity The speed at which data is generated and processed.
Data Variety The different types and formats of data being handled.

Data Roles

Data Analyst Answers business questions by analyzing data and creating reports and dashboards.
Business Analyst Translates business needs into data and reporting requirements.
BI Developer Builds automated dashboards and reporting systems for many users.
Data Engineer Designs and builds data pipelines that move and prepare data at scale.
Analytics Engineer Transforms raw data into analytics-ready models for BI tools.
Data Architect Designs the overall structure and flow of data systems.
Data Scientist Uses statistics and machine learning to predict future outcomes.
ML Engineer Turns models into scalable, production-ready systems.
AI Engineer Builds AI-powered systems using models, APIs, and tools.
ML Ops Engineer Automates deployment, monitoring, and lifecycle of machine learning models.
Data Product Manager Owns data products and aligns them with business goals.
Data Steward Ensures data quality, definitions, and standards are followed.
Data Owner Accountable for the correctness and usage of specific datasets.
Data Consumer Uses data products for analysis, reporting, or decision making.
Data Producer Generates data through systems, applications, or processes.
Data Quality Analyst Monitors and improves data accuracy and reliability.
Data Governance Lead Defines policies for data usage, privacy, and compliance.
Data Platform Engineer Builds and maintains shared data infrastructure and tools.
Chief Data Officer (CDO) Owns the data strategy across the organization.

Data Processing

Data Manipulation Modifying data through filtering, sorting, or aggregating.
Data Transformation Converting data into a suitable format for analysis or storage.
Data Cleanup Fixing data issues such as errors, duplicates, and missing values.
Data Enrichment Adding additional information to existing data.
Data Aggregation The process of summarizing data using functions like sum or average.
Data Modeling Designing how data is structured and related within a system.
Data Validation Ensuring data meets defined quality rules.
Data Quality Measuring accuracy, completeness, and consistency of data.
Data Standardization Converting data into a consistent format or convention.
Data Normalization Scaling numeric data into a common range or structure.
Data Deduplication Identifying and removing duplicate records.
Data Filtering Selecting only relevant data based on conditions.
Data Sorting Ordering data based on one or more fields.
Data Parsing Extracting structured values from raw or semi-structured data.
Data Encoding Converting categorical values into numeric representations.
Data Masking Obscuring sensitive data to protect privacy.
Data Sampling Selecting a subset of data for faster processing or analysis.

Data Architecture

Data Architecture The overall design of how data is collected, stored, integrated, and used across systems.
Data Platform The shared infrastructure, tools, and services used to manage data at scale.
Data Source The original system or application where data is created and collected, such as databases, APIs, or files.
Operational Data Store (ODS) A system that stores near real-time operational data.
Data Warehouse A central system for storing clean, structured data optimized for analytics.
Data Lake A storage system for raw data in any format, structured or unstructured.
Data Lakehouse A platform that combines data lake flexibility with data warehouse structure.
Data Mesh A decentralized architecture where domains own and share data as products.
Data Mart A subject-focused subset of a data warehouse designed for a specific business area.
Data Vault A modeling approach focused on historical tracking, scalability, and auditability.
Medallion Architecture A layered design using bronze, silver, and gold data layers.
Bronze Layer Stores raw ingested data with minimal transformation.
Silver Layer Contains cleaned and standardized data ready for business logic.
Gold Layer Holds curated, business-ready data for analytics and reporting.

Data Engineering

Data Engineering Building systems that collect, move, transform, and store data reliably at scale.
Data Pipeline An automated process that moves data from source to destination.
ETL Extract, Transform, Load data into analytics systems.
ELT Extract, Load, then Transform data inside the target system.
Data Extraction Copying data from source systems without changing it.
Data Transformation Converting data into the correct structure, format, and logic for analysis.
Data Load Writing extracted or prepared data into a target system.
Data Ingestion The process of collecting data from source systems.
Batch Processing Processing data in large chunks at scheduled intervals.
Streaming Processing Processing data continuously in real time.
Full Load Loading the entire dataset from a source system.
Incremental (Delta) Load Loading only new or changed data since the last run.
Schema Evolution Handling changes in data structure over time.
Partitioning Splitting data into smaller parts to improve performance.
Indexing Creating structures that speed up data access.
Orchestration Managing dependencies and execution of data pipelines.
Scheduling Running data jobs at defined times.
Monitoring Tracking pipeline health, failures, and performance.
Data Migration Moving data between systems while preserving accuracy.
Slowly Changing Dimension (SCD) A technique for managing changes in dimension data over time.
SCD Type 1 Updates data by overwriting old values without keeping history.
SCD Type 2 Preserves full history by creating a new record for each change.
SCD Type 3 Stores limited history by adding extra columns for previous values.
Schema Registry A centralized store for managing schema versions.
Schema Drift Unexpected changes in incoming data structure.
Change Data Capture (CDC) Capturing database changes in real time.
Data Freshness How up to date data is compared to when it was generated.
Data Snapshot A copy of data taken at a specific point in time.

Data Analytics

Data Analytics The practice of analyzing data to answer business questions and support decisions.
Business Intelligence (BI) Turning data into insights for decision making.
Descriptive Analytics Explains what happened using historical data.
Diagnostic Analytics Explains why something happened by analyzing patterns and relationships.
Predictive Analytics Estimates what is likely to happen in the future using data and models.
Prescriptive Analytics Recommends actions based on predicted outcomes.
Dashboard A visual interface showing key metrics and trends.
Report A structured presentation of data and insights.
KPI A key performance indicator used to measure success.
Metric A numeric measurement used to track performance.
Dimension A categorical field used to group and filter data.
Measure A numeric value that can be aggregated for analysis.
Slice and Dice Analyzing data from different perspectives.
Filter Restricting data to a specific subset.
Aggregation Summarizing data using functions like sum or average.
Trend Analysis Identifying patterns and changes over time.
Ad Hoc Analysis A one-time analysis performed to answer a specific question.
Data Visualization Using charts and visuals to communicate insights.
Insight A meaningful finding that supports better decisions.
Exploratory Data Analysis (EDA) Exploring data to understand patterns, distributions, and anomalies.
Drill Down Moving from summary data to more detailed data.
Drill Up Moving from detailed data to higher-level summaries.
Cohort Analysis Analyzing groups of users sharing a common characteristic.
Correlation Measuring how strongly two variables move together.
Outlier A data point that significantly differs from others.
Distribution How values are spread across a dataset.
Percentile A value indicating the relative position within a dataset.
Rolling Average An average calculated over a moving time window.
Self-Service Analytics Allowing users to explore data without technical help.
Storytelling with Data Communicating insights through narrative and visuals.

Data Science

Data Science Using data, statistics, and models to extract insights and make predictions.
Dataset A collection of data used for analysis or modeling.
Exploratory Data Analysis (EDA) Understanding data through statistics and visual exploration.
Machine Learning Algorithms that learn patterns from data automatically.
Supervised Learning Training models using labeled data.
Unsupervised Learning Discovering patterns without labeled outcomes.
Feature An input variable used by a model.
Feature Engineering Creating and improving features to boost model performance.
Model A mathematical representation learned from data.
Algorithm A method used to train a model.
Training Data Data used to teach a model patterns.
Test Data Data used to evaluate model performance.
Prediction An estimated output generated by a model.
Classification Predicting categories or classes.
Regression Predicting continuous numeric values.
Clustering Grouping similar data points together.
Evaluation Metric A measure used to assess model quality.
Accuracy How often a model makes correct predictions.
Precision How many predicted positives are actually correct.
Recall How many actual positives were correctly identified.
Overfitting When a model memorizes data instead of learning patterns.
Underfitting When a model is too simple to capture patterns.
Bias Error caused by overly simple assumptions.
Variance Error caused by sensitivity to training data changes.
Cross Validation Testing model stability using multiple data splits.
Hyperparameter A model setting defined before training.
Hyperparameter Tuning Finding optimal model settings.
Model Interpretability Understanding how a model makes decisions.
Model Drift Performance decline caused by changing data over time.

AI Engineering

AI Engineering Building production systems that apply AI models to real business problems.
AI Engineer A professional who designs, deploys, and maintains AI-powered systems.
AI Model A trained system that performs tasks like prediction, classification, or generation.
Model Inference Running a trained model on new input data to produce outputs.
Pretrained Model A model trained on large datasets and reused for new tasks.
Fine Tuning Adapting a pretrained model to a specific task or domain.
Large Language Model (LLM) A model trained on massive text data to understand and generate language.
Prompt An instruction or input given to an AI model.
Prompt Engineering Designing prompts to get accurate and reliable model responses.
Context Window The amount of information a model can consider at once.
Embedding A numeric representation of data used for similarity and search.
Vector Database A database optimized for storing and searching embeddings.
Semantic Search Finding results based on meaning rather than exact keywords.
RAG (Retrieval Augmented Generation) Combining external data retrieval with model generation.
AI Agent An AI system that can reason, decide, and take actions.
Tool Calling Allowing models to trigger functions, APIs, or external tools.
Orchestration Managing how multiple models, tools, and steps work together.
Inference Pipeline A workflow that processes input data through models and tools.
Latency The time it takes for an AI system to return a response.
Scalability The ability of an AI system to handle growing usage or data.
Model Serving Hosting models so they can be accessed by applications.
API Integration Connecting AI models to applications through APIs.
Evaluation Measuring the quality and reliability of AI outputs.
Hallucination When a model generates incorrect or fabricated information.
Guardrails Rules and controls that limit unsafe or incorrect AI behavior.
Monitoring Tracking performance, errors, and usage of AI systems in production.
Cost Optimization Managing compute and token usage to reduce AI costs.
Security Protecting AI systems from misuse or attacks.
Privacy Ensuring sensitive data is handled safely by AI systems.
Model Governance Policies and practices for managing AI models responsibly.

Data Governance

Data Governance The framework of policies and processes that ensure data is managed correctly across the organization.
Data Ownership Defining who is accountable for a dataset and its business meaning.
Data Stewardship Operational responsibility for maintaining data quality and standards.
Data Policy Formal rules that define how data should be created, used, and protected.
Data SLA An agreement defining data availability, quality, and timeliness expectations.
Data Completeness Whether all required data is present.
Data Consistency Ensuring data is the same across systems and reports.
Data Availability Whether data can be accessed when needed.
Data Standards Agreed formats, definitions, and conventions for data consistency.
Data Quality Management Processes to measure, monitor, and improve data quality.
Data Lineage Tracking where data comes from and how it changes across systems.
Data Catalog A centralized inventory describing datasets, tables, and columns.
Business Glossary A shared dictionary defining business terms and metrics.
Metadata Management Managing technical and business metadata for transparency.
Data Classification Categorizing data based on sensitivity and usage.
Access Control Defining who can view, edit, or use data.
Role Based Access Control (RBAC) Granting data access based on user roles.
Data Privacy Ensuring personal and sensitive data is handled legally and ethically.
Data Security Protecting data from unauthorized access or breaches.
Compliance Adhering to regulations such as GDPR or internal policies.
Auditability Ability to trace data changes for review and compliance.
Data Retention Rules defining how long data should be stored.
Data Archiving Moving inactive data to long-term storage.

I hope you like it and I wish you a wonderful day ❤️

Baraa

By the way, today I also released a very important Python video about functions.
This is a must-learn topic and one of the key things that separates professional developers from casual coders.

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

First of all, happy new year. I hope 2026 brings you growth and a lot of good energy.

And we’re kicking off the year with something special!!

I want to personally invite you to a live Databricks bootcamp I’m hosting on 17–18 January. → [ LINK ]

Databricks is becoming the go-to platform for many large companies to build their data strategy. I’ve seen this first-hand. I led one of the largest Databricks Lakehouse projects at Mercedes-Benz, where we delivered top-tier data products used by more than 40 analytics and AI teams. In this bootcamp, I’ll introduce Databricks from both perspectives, data analysts and data engineers, and show you how we actually use it in industry projects.

I decided to make this bootcamp completely free. To be honest, I see many bootcamps charging thousands, and I know that a lot of people simply cannot afford that. This bootcamp is for you. It’s built from real industry experience, not from documentation turned into slides.

Also want to be transparent with you. I have bigger plans around Databricks. I’m working on a full Databricks course for YouTube and the Data Academy, and I’m also planning a longer and deeper bootcamp over a full month for those who want a true deep dive. That extended bootcamp is still in planning and will be a paid.

What you’ll learn in these 2 days

You’ll see how Databricks works end to end in real scenarios:

• How data analysts use SQL and BI inside Databricks

• How data engineers build data pipelines using the Medallion Architecture

• How analytics, engineering, and AI work together in one platform

• How to think in layers, from raw data to business-ready insights

Who this bootcamp is for

• Anyone getting started with Databricks in a structured way

• Data Analysts

• Data Engineers

How to register

You can register here: → [ LINK ]

Bootcamp details

The live bootcamp will take place on:
Saturday 17 January and Sunday 18 January 2026
Time: 16:00–19:00 (CET)

All relevant links, materials, and access details will be shared by email a few days before the event. Please make sure to check your inbox and spam folder so you don’t miss any updates.

If you know someone who wants to become a data analyst or data engineer and can’t afford paid bootcamps, please share this with them.

I’m genuinely excited about this bootcamp. It’s going to be a lot of fun and very practical, and I’d love to see you live.

Have a wonderful day ❤️

Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

If you’ve been around data for a while, you’ve probably noticed it.
There’s an endless debate about Power BI vs Tableau.

I see it on YouTube. On social media. Inside companies.
People compare features, prices, visuals, performance. And the question always comes up.

Which one is better?

Here’s my honest answer.
Both are good.

I’ve worked on projects where we used only Power BI.
Other projects where we used only Tableau.
And I’ve even worked on a project where we used both at the same time.

That’s not strange at all. That’s just how analytics works. Different problems need different tools.

But something interesting has been happening over the last few years.
Since around 2021, I’ve seen a clear shift in many companies toward Power BI.

Not because Tableau suddenly became bad.
But because Power BI fits better into how most teams actually work.

Let me explain what I keep seeing in real projects.

Why many teams choose Power BI

It fits naturally into daily work tools

Power BI fits right into how most teams already work.

Outlook. Teams. Excel. PowerPoint. SharePoint.
Power BI feels familiar from day one. It looks like Excel. It feels like PowerPoint. And it connects to these tools without extra effort.

If you ever opened Power BI and thought, this feels familiar, that’s the reason.

Switching from Excel is easier than expected

If you already use Excel, you’re not starting from zero.

A lot of the logic feels familiar.
DAX feels familiar. The interface feels familiar. Even the charts feel familiar.

That makes the switch much easier for most people.

Cost matters more than people admit

Licenses are a big part of company budgets. Especially now.

Power BI is simply more affordable than many other tools.
For a lot of teams, that alone makes the decision easier.

Everything in one place

With Power BI Desktop, you do everything in one tool.

You clean data. Prepare it. Model it. And build reports.
Power Query is already there.

With other tools, you often need an extra product just for preparation. That means more tools, more licenses, and more context switching.

Easy integration with cloud platforms

This one shows up a lot in real projects.

Power BI fits very naturally into cloud platforms, especially Azure.
Azure SQL. Synapse. Data Factory. Data Lake. Fabric. Everything connects smoothly.

For teams already running on Azure, Power BI often feels like the default choice.
Less setup. Fewer connectors. Fewer surprises.

It also plays well with modern data stacks.
Lakehouses. Incremental refresh. Semantic models. Row-level security.

That tight cloud integration makes life easier for data engineers and BI teams.
And over time, that simplicity matters more than people expect.

Microsoft keeps investing in it

Microsoft clearly puts a lot of focus on Power BI.

There are updates every month. New features. New visuals. New connectors.
You can feel the tool evolving fast.

Strong data modeling

This is my favorite part.

Power BI is very strong when it comes to data modeling.
You can build proper models, relationships, and measures that scale well.

When the model is strong, everything else becomes easier.

Compared to that, Tableau is more limited in how you model data. And in real projects, this makes a big difference.

Where Power BI struggles

Complex visuals can feel limiting

For basic operational reports, Power BI works great.
Bar charts, line charts, tables, filters. No problem.

But once you move into advanced analytical visuals, things change.

If you’re a data scientist building dot plots or exploring complex patterns, Tableau is often faster and more flexible. It still wins there.

Data point limits are a real issue

This one comes up often.

In Power BI, visuals like scatter charts are limited to a few thousand points.
That makes it hard to explore large datasets or spot outliers properly.

In Tableau, I’ve built visuals showing millions of points. No hard limits. No workarounds.

Mac users need workarounds

Power BI Desktop does not run natively on Mac.

You always need a workaround.
With Tableau, you install it and it just works. Windows or Mac.

Why we sometimes use both

These limits are exactly why, in one project, we used both tools together.

Each tool solved a different problem.
And that’s often the smartest approach in real analytics work.

How To choose between Tableau and Power BI

Power BI fits most everyday reporting

Power BI sits where most reporting actually happens.

• Standard reports

• Operational dashboards

• KPIs for managers and leaders

This easily covers around 80 percent of reporting needs in most companies.

The users here are usually standard business users.
They want clear numbers and quick answers, not complex exploration.

Power BI works well because it is structured, easy to maintain, and easy to share.

Tableau shines in advanced analytics

Tableau lives in a different space.

• Advanced analytics

• Highly customized visuals

• Large datasets and big data

This is usually used by expert users.
Analysts and data scientists who explore data deeply.

Here, Tableau feels faster and more flexible.
You can experiment more and push visuals much further.

Two tools, two roles

This is why I don’t see it as a fight.

Power BI dominates standard and operational reporting.
Tableau shines in advanced analytical exploration.

They solve different problems for different users.

But things are shifting.

As more companies move to the cloud, especially around Microsoft and Azure, Power BI is becoming the default choice for many teams. Not because it replaces everything, but because it fits most everyday needs better.

And since most reporting in real companies is still standard and operational, Power BI naturally keeps gaining ground.

Thanks for reading, and I wish you all a happy Christmas and a great New Year.

Have a wonderful day ❤️

Baraa

By the way, I’ve started a new full Power BI course where I deep dive into the topics I mentioned here. You can follow the series here.

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

A friend called me a few days ago and asked a question that a lot of people think about quietly.
He said he wants to become a data analyst, but he has no degree, no experience, and he feels too old to start. He is almost 40. He told me it feels impossible.

And this is the part that hurts the most.
The job market feels tough. Companies ask for experience. And people fall into this loop.

I need experience to get a job, but I need a job to get experience.

If you are reading this and you feel the same, I want you to know something. It is possible. I have seen it many times. I have interviewed hundreds of candidates in my previous roles, and many of them got hired without a degree or experience.

But the game changed. The old way no longer works.
Just doing a few courses and applying for hundreds of jobs is not enough anymore. Today you have to show how you think, how you work, and how you solve problems. You have to give companies something real to look at.

So now let me show you how to do it!

The Two Phases

The Roadmap has two simple phases.

• Phase1: you work in a sequential way. One skill at a time. This keeps things clear and easy to follow.

• Phase2: Once you start applying for jobs, you switch to working in parallel. You apply, improve your skills, update your projects, and grow your portfolio at the same time.

Phase 1: Sequential. One thing at a time

Step 1: I can work with data
You learn Excel, SQL, and a visualization tool like Power BI or Tableau.
This gives you the basics of cleaning data, exploring data, and showing insights.

Step 2: I can think like an analyst
You build two projects.
An EDA project in SQL and a dashboard project in Power BI or Tableau.
These teach you how to explore questions and explain what the data is telling you.

Step 3: I have proof
You collect your work in a simple portfolio and add a certificate if you want.
Now you can show real evidence of your skills.

Step 4: I look hirable
You prepare a clean one page resume and update your LinkedIn.
This makes it easier for companies to understand who you are and what you can do.

After these four steps, you move to Phase 2 and start applying for jobs.

Phase 2: Parallel. Multiple things at the same time

Phase 2 starts the moment you begin applying for jobs.
Your goal now is to build momentum. You improve your profile, your skills, and your projects all at once.

30 percent: Learn advanced topics
You go deeper into SQL and Power BI, and you start learning Python with Pandas.
You also add some basics of AI and prompt engineering.
This keeps your skills growing while you apply.

50 percent: Extend or build new projects
You improve the projects you already have or create new ones with better data and better analysis.
Strong projects make you stand out more than anything else in this phase.

10 percent: Improve your portfolio
You update your write ups, clean your explanations, and make your work easier to understand.
Small changes here make a big difference.

10 percent: Customize your resume and LinkedIn
You tune your resume for each application and stay active on LinkedIn.
This helps recruiters find you and understand your strengths quickly.

How to Handle Interviews

Interviews can feel stressful at the start, but they get easier with practice. Think of each one as a learning step, not a test. You learn what to say, what to fix, and how to explain your work more clearly.

The most important thing you prepare is your project story.
You explain the problem, the tools you used, the steps you took, and what you found. Walk them through your thought process. This is what tells the interviewer that you can think like an analyst and solve real problems.

Be honest about what you know and what you do not know.
Confidence here does not mean being perfect. It means showing ownership of your work and staying calm as you explain your steps.

And remember that companies are not only looking for technical skills. They want to see how you approach problems, how you communicate, and how you work through uncertainty. These parts often matter more than any single tool or function.

If you treat interviews as conversations, not pressure moments, you will perform much better. Each one moves you forward.

So my Friends…

You can start this path with no degree and no experience. I have seen many people do it. The roadmap is simple. You learn the core skills, build real projects, create your proof, and keep improving while you apply. It takes time and patience, but it works.

Thank you for reading. I hope this guide gives you a bit more clarity and helps you take your next step with more confidence.

Have a wonderful day ❤️

Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

One last release before 2025 ends:

🎉 Power BI Crash Course from absolute beginner is now LIVE & Free! 🎉

It’s under one hour and takes you through the full end to end process using the whole Power BI ecosystem: Desktop, Service, and Mobile.
This is made for total beginners. No heavy theory, no deep dives. Just a clear walkthrough on all Power BI Components.

What’s Inside the Course?

1. Power BI Desktop

This is the core tool where you build everything.

• Install Power BI Desktop

• Connect data from files

• Explore the Model View and understand relationships

• Inspect your tables in the Table View

• Create basic transformations with DAX

• Clean and prepare data using Power Query

• Build visuals in the Report View and create your first dashboard

2. Power BI Service

This is where you publish and share your work.

• Create a temporary business email

• Create your Power BI Service account

• Publish your work from Desktop to the cloud

• Open and interact with your report in the browser

• Learn the Service interface including workspaces, semantic models, and lineage

3. Power BI Mobile

This is how you access your dashboards on the go.

• Install the app

• Log in with your business account

• View and interact with the same dashboard on your phone

So my Friends…

This session was only the starting point.
Now that you understand the full workflow, the next phase is to slow down and study each part of Power BI properly.

I will start releasing deep dive lessons on every topic you saw.
Desktop, data modeling, DAX, Power Query, visuals, publishing, everything.
Step by step, with real examples, the same way I built the SQL and Tableau courses.

The goal is to turn this into a complete Power BI course that covers the full journey from beginner to advanced.
So if you enjoyed the crash course, stay with me because the detailed lessons are coming next.

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

Around 2003 in university, SQL was all theory. Books, long lectures, memorizing syntax. No projects. No datasets. No tools. And of course no AI. It was slow and honestly a bit boring.

Today things are much easier.

If I had to learn SQL again in 2026 and I wanted to do it fast, this is the exact path I would follow. One for data analysts and one for data engineers.

Let’s go.

Why Learn SQL?

Before we go into the roadmap, I want to address the biggest question people ask today: Why do we still need to learn SQL when we have AI?
Yes, AI can write SQL. But it also makes mistakes. If you cannot review the logic, you will deliver bad insights. And bad insights lead to bad decisions.

SQL is still one of the most important skills in the entire data world. Data analysts use it every day. Data engineers depend on it to build pipelines. Data scientists need it to prepare datasets.

And almost every modern data tool today supports SQL because it is the easiest way for people to work with data.

This skill is not going anywhere.

The 2x Golden Rule

Here are two simple rules to keep in mind:

1. Spend 20 percent of your time watching and 80 percent writing your own queries.

2. Do not try to memorize syntax. Focus on understanding how the data moves through your query. The logic matters more than the keywords. Syntax is easy to look up.

With the right mindset, SQL becomes simple. Now here is the roadmap.

Data Analyst Roadmap To Learn SQL

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1. Setup

Start with one course and one instructor. Do not mix ten different tutorials.
Pick one database engine. SQL Server or PostgreSQL is perfect.
Create a small plan in Notion so you know what you are doing each week.

Resource: SQL Full 30-Hours mastery

2. Basic SQL Topics

Learn the core building blocks. These will carry you through 80 percent of your work as a data analyst:

• SELECT: This is how you pull data from a table and answer any question.

• WHERE: Lets you filter rows so you find exactly what you need.

• ORDER BY: Helps you sort results to spot patterns.

• LIMIT: Useful when you work with large tables and want quick previews.

• JOINs: (INNER, LEFT, RIGHT, FULL): Lets you combine tables to get complete insights and focus on INNER and LEFT.

• GROUP BY: Groups data so you can calculate metrics.

• Aggregations (SUM, AVG, COUNT, MIN, MAX): Turns raw data into meaningful numbers.

Now Close the Course and start practicing, it is between you and ChatGPT!

Prompt:
Act as my SQL mentor and query coach.

My current SQL skills:
- Window Functions
- Date Functions
- CTE
- Subqueries

Your task:

- Give one SQL challenge at a time.
- Start simple and increase difficulty step by step.
- Each challenge must use at least one of the skills above.

For each challenge, include:

1. SQL Task: What I need to find.
2. Sample Data.
3. Keep it short and direct.

Rules when I send my query:

- Review correctness, readability, efficiency, and best practices.
- Suggest clear improvements.
- Give a score out of 10 with a short explanation.
- Then give the next challenge, slightly harder than the previous one.
- If I ask for a hint, give only a small clue, not the full answer.

Tell AI what you’ve learned and ask it for one SQL challenge at a time. Write your own query, get a quick review, then repeat the loop with slightly harder tasks so you improve fast.

3. Advanced SQL Topics

Now you move from basic reporting to real analysis. These topics help you answer harder questions with clean, readable queries:

• Window functions: Use things like ROW_NUMBER, RANK, LAG, and LEAD to compare rows, track changes over time, and find top or bottom performers.

• Date functions: Helps you extract, format, and group dates so you can answer questions like last week, last month, or previous quarter.

• CTEs (WITH): Break big queries into smaller steps so they are easier to read, debug, and explain.

• Subqueries: Use a query inside another query when you need a result first, then filter or compare based on it.

Learn these on top of the basics and you can handle most real data analyst work in SQL.

And once you learn these, close the course again and repeat the same practice loop with AI so you build speed and confidence.

4. Build Portfolio Projects

Now it is time to take everything you learned and turn it into real work you can show.
These simple projects will make your SQL skills visible and help you stand out.

• Start with an EDA project: Pick one dataset. Clean it, explore it, calculate metrics, and answer a list of business questions using only SQL.

• Create one analytical project: Build a second project where you compare trends, find insights, and show how SQL can solve a real problem.

• Document everything: Add your queries, notes, and results to GitHub so people can see how you think and how you work.

Keep it simple. Keep it clean. Two strong SQL projects are more than enough for your first portfolio.

And once you finish them, share your progress. It keeps you motivated and helps others learn from your journey.

Data Engineer Roadmap To Learn SQL

The first two steps are exactly the same as the Data Analyst path.
The advanced topics shift a bit because data engineers work with bigger systems and more complex pipelines:

• Window functions help you compare rows, track changes, and prepare data for downstream tasks.

• String functions let you clean and reshape messy fields before loading them into your data models.

• CTEs and Subqueries help you break down heavy logic into smaller steps so your transformations stay readable.

• Views act like reusable building blocks that hide complexity and make pipelines easier to maintain.

• Indexing and Partitioning help you speed up queries and handle large tables without slowing the system down.

And once you learn these topics, practice the same way.
Ask AI for one challenge, write your own query, get feedback, then move to a slightly harder task.

For your final project, build a small data warehouse using SQL.
Create a few dimension tables, one fact table, load simple data, and write queries that show how everything connects.
It does not need to be huge, it just needs to show that you understand how data flows inside a real system.

So my Friends…

So my friends, that’s the full picture. If you follow this path, you’ll learn SQL way faster than the old days of books and lectures. It’s simple, it works, and it gets you ready for real projects.
Thanks for reading.
Baraa

Thanks for reading. ❤️
Baraa

Also, here are 4 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

I’m really excited to share this with you. I’ve officially started working on my new PowerBI Course!

Power BI is one of the most requested courses on my channel, and for a good reason.

This is the tool I have used since 2019 on real projects at Mercedes Benz and now It is time to share everything I learned in my projects with You. The small things. The decisions. The mistakes. The parts that actually matter when you work with real data.

So as usually I explain things using animated sketches. So Let’s go!

Power BI is one of the most requested courses on my channel, and for a good reason. It sits right between the business world and the technical world. If you understand it well, you can answer real questions, guide teams, and build reports that people trust.

This post gives you the full picture. What Power BI is, why it exists, who uses it, where it shines, and where it falls short.
So.. Let’s GO!

1. The Chaos - Without BI

Before a company grows, everyone tries to answer business questions in the same simple way. Managers want to understand why sales dropped, which region is struggling, or where they should invest next, and they ask the data analyst to find the answers.

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The analyst starts the usual way. They export the data into Excel, clean it, fix the mistakes, merge the sheets, and try to build something that explains what is going on.

It works in the beginning, but once the company grows, the process starts falling apart. Different departments create their own files. Each version is slightly different. Reports take days or even weeks. The data inside becomes old fast. And managers end up making decisions based on numbers that no longer match the reality of the business.

Here are the core problems teams run into:

• Everything is manual and depends on repeated human work.

• Reports become outdated as soon as they are finished.

• There is no automatic refresh, so every update is done by hand.

• Connecting to modern sources like cloud systems or APIs is difficult.

• Large datasets cause Excel to slow down, freeze, or crash.

• Teams cannot collaborate in one file and end up with many versions.

• Sharing files puts sensitive data at risk.

• There is no access control, so everyone sees everything.

• Different teams create different numbers, so managers get conflicting answers.

At this point companies start to realize they need a better system (BI) to handle their data.

What Business Intelligence (BI)

Once you see this chaos a few times, you start to understand why companies move away from spreadsheets. They cannot run a business like this. They need a standard process. Something reliable.

That process has a name. Business Intelligence. Or BI.

BI is not just charts. It is the full chain.
Collecting the data. Cleaning it. Structuring it. Building logic on top of it. Turning it into something people can understand. And then publishing it so others can use it to make decisions.

This full workflow is BI.
Power BI and Tableau are two of the major tools that support this process.

Most people only see the dashboards. But the real work happens behind the scenes.

The History Behind Power BI

Here is the same clean bulletpoint list, now with dates added where historically accurate and still keeping your simple, direct tone:

• Early 2000s: Excel already had strong internal tools like Power Query and Power Pivot, but they were hidden and not widely used.

• 2010 to 2013: Tableau started rising fast and became popular because it made data visualization simple and clear.

• 2013 to 2014: Microsoft realized Excel could not handle modern data loads, cloud sources, or large models, and that they needed a new standalone analytics product.

• 2014: Microsoft began rebuilding the strongest parts of Excel into a separate tool focused on data analysis.

• July 2015: Power BI officially launched as a standalone BI platform.

• 2015 to 2019: Power BI grew month after month with new visuals, connectors, modeling features, and tighter integration with the Microsoft ecosystem.

• 2020 to 2021: Power BI became a major competitor to Tableau, especially in enterprise environments.

• 2023: Microsoft introduced Microsoft Fabric and Copilots, expanding Power BI into a larger analytics platform.

• Today: Google Trends shows Power BI nearly matching Tableau in global interest, reflecting a real shift in how companies handle data and build analytics workflows.

What is Data Visualization?

Raw data is hard to understand on its own. A table full of numbers looks flat, and your brain has to work hard to make sense of it. But once you turn that same data into visuals, everything changes. A chart can show a trend in a second. A map can highlight a pattern you never noticed. And a simple shape can reveal a problem that was buried inside rows of numbers.

The reason is simple. Our brain processes visuals much faster than text. When you see an image, you understand it instantly. But when you read a word, your brain first has to convert it into a picture before it makes sense. That extra step slows everything down. This is why we remember only a small part of what we hear, a bit more of what we read, and almost everything we see.

Good visuals are not just nice to look at. They help you explain your ideas with clarity. They make complex topics easier to follow for people who are not technical. They help you spot patterns, risks, and opportunities that are impossible to see in raw tables. And they support better decisions because everyone sees the same story in front of them.

This is the real power of data visualization. It turns information into something people can understand, remember, and act on.

The Components of Power BI

Power BI looks simple from the outside, but it is built on five clear steps that connect the whole analytics workflow. Each part has its own job, and you use all of them in almost every project.

1. Power Query
This is where everything starts. You clean the data, fix the errors, remove duplicates, and prepare the tables so they are ready to work with.

2. Modeling
After the data is clean, you structure it. You connect the tables, define relationships, and build the model that keeps everything accurate and fast.

3. DAX
Here you write the calculations. Metrics, KPIs, percentages, comparisons. DAX creates the business logic that the visuals depend on.

4. Visuals
This is the first part people actually see. You turn the model and the logic into charts, cards, tables, and filters so the story becomes clear.

5. Share
When the report is ready, you publish it so managers and teams can view it in the browser, on their phone, or inside their daily tools.

These five steps cover the full journey. You bring the data in, clean it, structure it, analyze it, visualize it, and share it in one flow.

Ecosystem of Power BI

Power BI is not just one tool. It is a small ecosystem, and each part plays a different role in how reports are created, published, and used inside a company.

Power BI Desktop
This is where the work happens. You clean the data, build the model, write DAX, and design the visuals. It runs on Windows, and this is the app you use to create the actual report.

Power BI Service
Once the report is ready, you publish it to the cloud. This is where your dashboards live. Teams open them in the browser, interact with the visuals, and check the numbers whenever they need to.

Power BI Mobile
Managers and decision makers can open the same dashboards on their phones or tablets. It is the fastest way for them to stay updated without opening a laptop.

Together, these three parts create one simple flow.
You build the report on your computer, publish it to the cloud, and teams across the company can view and use it from anywhere.

Who Uses Power BI

Power BI is not only for data analysts. It is used across many roles, each for a different reason.

Data Analysts
They use Power BI to answer the main business questions. Why something happened, where the problem is, and what the numbers are telling the team.

Data Engineers
They use it to monitor pipelines, check data quality, and make sure everything is running the way it should.

Data Scientists
They use Power BI to present their model results in a simple way that non technical teams can understand.

Business Analysts
They use it during discussions with stakeholders to explain what happened, why it happened, and what might come next.

Managers and Leaders
They use it to check performance, budgets, and key metrics without digging through raw data.

Individuals
Many people even use Power BI privately at home to track spending, budgets, or personal projects.

Power BI fits any situation where you have numbers and you want to understand the story behind them.

Why Power B

There are many BI tools out there, but Power BI keeps showing up in real companies for a few clear reasons.

It fits into your daily tools
Most people already use Outlook, Teams, Excel, SharePoint, and PowerPoint. Power BI feels natural because it works the same way and connects to everything without extra steps.

It is affordable
Licenses cost less than most alternatives. For companies trying to reduce software expenses, this alone is a strong reason to choose it.

Easy to switch from Excel
If you know Excel, you already understand a big part of Power BI. The transition is simple because the concepts and the interface feel familiar.

Everything in one place
Data cleaning, modeling, DAX, and visuals all live inside the same tool. You do not need separate apps to prepare or analyze the data.

Continuous updates
Microsoft pushes new visuals, connectors, and features every month. The tool grows fast and adapts to new workflows, including AI features.

Strong data modeling
Power BI gives you a powerful modeling layer. This makes your reports faster, more flexible, and easier to maintain when the data becomes complex.

These are the practical reasons why so many teams choose Power BI for their analytics work.

Power BI Is Not Perfect!

Power BI is great for most reporting needs, but it is not perfect. There are a few situations where another tool, like Tableau, works better.

Limited for advanced analytics
Power BI is strong for standard operational reports, but it struggles with more complex analytical visuals that data scientists or advanced analysts often need.

Limits on data points
Scatter plots in Power BI can only show a limited number of points. If you want to display hundreds of thousands or even millions, the tool cannot handle it.

Tableau handles big visuals better
Tableau does not enforce these limits. You can load very large visuals with millions of data points and still explore the patterns clearly.

Desktop only on Windows
Power BI Desktop does not run on Mac. You need workarounds. Tableau works on both Windows and Mac without extra steps.

These are the practical situations where Power BI falls short and where teams often choose Tableau for the more advanced parts of their work.

So my Friends…

If you understand this much about Power BI, you already see why companies rely on it every day. It organizes the chaos, makes the data usable, and gives teams a single place to understand what is really happening.

We will go deeper in the next lessons. Desktop, DAX, modeling, real examples, and the practical workflow that actually works in a real job.

Thanks for reading. ❤️
Baraa

Also, here are 3 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap
📌 AI Engineering Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now

There is a lot of noise around AI engineering, and most of it makes the journey look harder than it really is. And to be honest, most of the “AI Engineering” roadmaps you see online are actually Data Science roadmaps with a different title.

That is why I decided to build a proper roadmap myself. Over the last years, I’ve worked on multiple AI projects in industry, and I’ve seen exactly what companies expect from a real AI engineer.

The [Full Notion Roadmap] is simply a reflection of that. Just the practical steps that match the work.

Let’s go.

What AI engineering really means

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AI engineering is a builder role.

You are not training models, designing networks, or doing research. Companies already have strong models available. Your job is to take these models and turn them into something useful inside the business.

That means connecting the model to company data, integrating it with internal tools and workflows, and giving people a clean way to use it. And while doing that, you make sure the system is safe, fast, and reliable.

This is why AI engineering feels closer to data engineering and backend work than to data science. Once you see it this way, the whole roadmap becomes clear.

AI Engineering Roadmap

Now that you know what the role actually looks like, here is the path I recommend. These are the phases that will take you from the basics to building real AI systems, and I’ve split everything into four clear phases. You can open the full Notion roadmap here: [LINK]

• 🌱 #1 Foundations
  Learn Python and Git GitHub so you can build a solid technical base and write code with confidence.

  🧠 #2 LLM Model Interaction
  Master prompting and APIs like OpenAI and Hugging Face so you can talk to models, control them, and integrate them into your own code.

  🧩 #3 Build and Run AI Systems
  Learn LangChain, RAG, AI agents, MCP, and basic LLMOps so you can build complete AI systems that understand your data and run reliably.

  💼 #4 Career
  Build strong portfolio projects, upgrade your LinkedIn profile, and earn certifications so you can stand out in today’s competitive market.

let’s deep dive into each phase.

#1 The Foundations (Python and Git)

Before you build anything with AI, you need a simple foundation. And that foundation is Python.

Almost every tool, model, and workflow in AI runs on Python. You do not need to master every corner of the language, but you should feel comfortable with the essentials. Lists and dictionaries, loops, functions, working with JSON, and making API requests. These few things show up in almost every AI project.

Once you start writing code, you also need a place to store it and manage it. That is where Git and GitHub come in. Create a repo, push your work, write clear commit messages, and use branches when you try new ideas. It keeps your work organized and makes you look professional when you apply for jobs.

With Python and Git in place, you have everything you need to start building real AI projects.

#2 LLM Model Interaction

Once you have the basics in place, the next step is learning how to work directly with AI models. This is where AI engineering really begins, because now you move from writing normal code to controlling intelligent systems.

Prompt Engineering

Prompt engineering is the part most people misunderstand. It is not typing a short phrase into a chat window and hoping for a good answer.
It is clear communication.
It is giving context, describing the task, setting the role, and showing examples of what you want.
Good prompts make the model stable and predictable. Weak prompts make the model chaotic.
This skill becomes the backbone of every AI workflow you will build later.

OpenAI API

After you understand prompts, the next step is learning how to call models through an API [LINK].
This is when you stop “using AI” and start “building with AI”.

Calling a model from Python lets you integrate AI into your own tools, scripts, and applications.
You send a structured request, receive a response, and use that output anywhere you want.
It is a small technical step, but it opens the door to real engineering work.

Hugging Face

Read more

Today I’m starting something I’ve wanted to teach for a long time. Power BI.

This is the tool I have used since 2019 on real projects at Mercedes Benz. It helped me explain problems to managers, monitor pipelines, and turn confusing datasets into something people could actually use. And now I finally get to break it down for y…

Read more

There are two moments in my career where technology shifted under my feet, and each time the lesson became clearer: tools are temporary, skills are permanent.

If you have worked in a company, you have probably noticed the same pattern.
Teams love to fight over tools.

One group adopts a new platform because the market promises it will “change everything.”
Another group picks a different tool because it came with better demos and louder hype.

Then the battles begin.

Endless comparison meetings.
Slide decks defending why “our tool” is the future.
Architecture committees arguing over names, logos, and pricing models.

I have been in too many of those rooms.
I cannot even count the hours spent debating:

• Talend vs Informatica.

• Data Factory or Databricks Workflows.

• Tableau vs Power BI.

• Azure vs AWS.

So much time wasted proving which tool is better, while the business still waits for value.

I did the same early in my career. I thought a tool decision was a career decision.

Years later, I realized there was a deeper truth hiding underneath all those logos and debates.
A truth I only learned through real transitions, real projects, and real stress.

These stories travel with me everywhere. I want to share them with you here, privately, because they shaped who I became as an engineer, a teacher, and a builder.

Story 1: Data Engineering Tools That Disappeared

I started my career as an ETL developer. Back then, pipelines were still called “ETL jobs,” and my entire identity was tied to one tool: Informatica.

Three years of deep mastery.

Mappings.
Reusable transformations.
Staging layers.
Performance tuning.

I genuinely believed I had built a bulletproof future. I was “the Informatica guy.” Then came the announcement:

“We are moving to Talend.”

All the pride and comfort I had built suddenly felt fragile. I had to start again, learn again, rebuild confidence again.

So I learned Talend. Jobs, contexts, lookups, dependencies. Within a few months, I became fast again. Almost too fast. Because a pattern was forming that I did not yet recognize.

I later joined another company that used IBM DataStage. Another tool to memorize. Another UI. Similar logic hidden under different buttons.

Then the cloud wave arrived.
Azure Data Factory became the star.
Then Databricks Workflows took over.

Each platform came with rebranding, hype, new terminology. Yet every time I opened a pipeline, the same truth appeared:

Only the colors and icons changed.
The engineering never did.

After almost a decade of chasing ETL tools, the realization finally broke through:

Tools rotate every few years.
Concepts remain unchanged.

Every job I built, in every environment, relied on the same core foundations:

• Incremental vs. full loads

• Orchestrating dependencies

• Debugging failures

• Reprocessing strategy

• Modular and reusable logic

• Delivering trusted data at scale

• Data quality checks and gates

No company today asks me about my DataStage expertise. Some of the tools I once mastered are not even mentioned on modern résumés.

Yet the skills behind them carried me into every transition with confidence.

I will be honest. Tool expertise has helped me get hired at times. It opened certain doors. However, what helped me grow inside those rooms was never the tool. It was the thinking:

Architecture.
Engineering principles.
Conceptual understanding.
The ability to design for the business.

That is the part of the career that compounds.

Tools fade.
Skills stay.
Thinking scales.

Story 2: BI-Tools That Promised Magic

My first real experience with Business Intelligence was MicroStrategy. Enterprise-level deployments. Complex security roles. Cubes that took hours to build. Even a simple report felt like assembling an airplane. If something broke, only a tiny group of specialists could fix it.

Then came the shift:

IBM Cognos.

New interface. New documentation. New certifications. Yet the same core challenges hiding underneath.

A few years later, the “modern BI revolution” arrived.

Tableau made insights feel effortless. Drag. Drop. Magic.
Then Power BI exploded and suddenly the entire industry was fighting over one question:

“Which one is better: Tableau or Power BI?”

Endless debates. Endless Meetings, Endless comparison videos.
Everyone certain their tool would dominate the future.

It reminded me of the ETL Tools story.
Tools rise. Tools disappear. Another one always comes next.

Believe me: if you know Tableau, you can learn Power BI in two days. And vice versa.

Whether the logo is yellow or blue, the real work behind every useful dashboard never changes:

• How is the data modeled

• What story must these numbers tell

• Which chart fits the question

• What decision should this view support

• What must be removed to make the insight clear

• How to tell story behind data

BI tools are only drawing instruments.
Analytical thinking is the art.

That skill survives every replatforming.

So my Friends…

When I look back at these two stories, a pattern becomes almost painfully clear.

I spent years learning tools that no longer exist.
Not a single one stayed with me.

Yet the thinking behind the work still drives every decision I make today.

Whether I was building pipelines in Informatica or Databricks, designing dashboards in MicroStrategy or Power BI, the real job never changed:

Understand the business.
Model the data properly.
Design simple systems that others can trust.
Communicate clearly.

That is the part of the career that compounds.
That is what makes you valuable in every room you enter.

I want to be very clear about one thing:

Learning tools is not bad.
We all need tools. Tools open the first door.

The danger is when someone builds their entire identity around one platform.

Do not be “the Power BI guy” whose future disappears the moment Power BI does.
Do not be “the Databricks girl” who panics the day another cloud platform arrives.

Be the person who can think, who can adapt, who can design, no matter the logo on the screen.

Thank you for reading and for being here with me in this private space.
Your trust means a lot. I hope this brought clarity to where your energy should truly go next.

We will keep growing together… the right way. ❤️
Baraa

This week we are entering the 𝗺𝗼𝘀𝘁 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 chapter in this entire 𝗣𝘆𝘁𝗵𝗼𝗻 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲s series.
𝗟𝗮𝗺𝗯𝗱𝗮 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀.

This is the technique that separates beginners from true Python 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀. Lambda gives you the power to write small, efficient, anonymous functions right inside your code like a pro

Also, here are 3 complete roadmap videos if you’re figuring out where to start:

📌 Data Engineering Roadmap
📌 Data Science Roadmap
📌 Data Analyst Roadmap

Hey friends —

I’m Baraa. I’m an IT professional and YouTuber.

My mission is to share the knowledge I’ve gained over the years and to make working with data easier, fun, and accessible to everyone through courses that are free, simple, and easy!

Visit My Youtube Channel

Subscribe now