Data engineering is the work of moving, organizing, and preparing data so a business can actually use it. That sounds technical, but the core idea is simple. Companies collect huge amounts of information every day, and someone has to make that information clean, reliable, and ready for analysis.
That “someone” is often a data engineer. In Christopher Garzon’s overview, the field comes down to one big job: building the behind-the-scenes systems that let data flow where it needs to go. If you’re curious about data careers, trying to switch into tech, or deciding between analyst, engineer, and scientist roles, this is a solid place to start.
Christopher Garzon, CEO of Data Engineer Academy, explains the basics through advance examples, including how data engineering compares to software engineering, which tools matter most, and why many people start as data analysts before moving deeper into the field.
What data engineering actually is?
A simple way to understand data engineering is to picture a house. You notice the walls, the floors, the furniture, and the decor first. What you usually don’t notice are the pipes and the wires under the surface. Still, those hidden systems keep the whole house running.
That is the analogy Garzon uses for data engineering. Data engineers build the hidden infrastructure. They create the systems that move data from one place to another, clean it up, and make it usable for the people who need it.
In that same analogy, data is like water or electricity. It powers the business, even if you don’t always see it directly. Sales dashboards, fraud alerts, app reports, forecasts, and machine learning systems all rely on data moving through the right channels.
To explain the scale of the problem, the fact points to a striking number: 407 million terabytes of data are created every day. If one terabyte were a small marble, that would be enough to fill a stadium, then another, then another. The exact number will change over time, but the point holds. Data creation is massive, and it keeps growing.
So what counts as data? Almost anything that can be recorded.
- Your phone’s health app tracks steps, miles walked, and heart rate
- A video platform tracks views, clicks, saves, and watch time
- A transcript turns spoken words into text data
- A bank logs purchases, times, locations, and account activity
Data engineers make data usable, and that makes analysis possible.
In other words, data engineering is the layer that supports everything else. Without it, businesses still collect data, but they can’t trust it, move it, or use it well.
How data engineers differ from analysts and data scientists
The easiest way to separate these roles is by looking at where each one works in the data process. Data engineers build and maintain the systems. Analysts and data scientists use the prepared data to answer questions and find patterns.
Here is the simplest comparison:
| Role | Main focus | What they produce |
| Data Engineer | Builds pipelines and infrastructure | Clean, usable, accessible data |
| Data Analyst | Explains business performance | Reports, dashboards, insights |
| Data Scientist | Models patterns and predictions | Forecasts, experiments, ML models |
The house analogy still works here. The data engineer handles the pipes and wiring. The analyst or scientist works with the finished environment once it functions properly.
That doesn’t mean the jobs never overlap. In real companies, they often do. A data analyst might write SQL that feels technical. A data engineer might spend time understanding business needs. A data scientist may need to move or reshape data too. Still, the main difference is this: engineers prepare the data, and analysts or scientists interpret it.
This distinction matters because many people enter tech without knowing which lane fits them best. If you enjoy structure, systems, movement of data, and reliability, data engineering often makes sense. If you prefer reporting, trends, and business questions, analysis may feel like a better first move.
The core tools and concepts every beginner should know
You don’t need to learn every tool at once. The foundation is actually pretty straightforward. Garzon highlights a few skills that come up again and again in data engineering: SQL, Python, data modeling, system design, and cloud platforms.
SQL and Python are the starting point
For most beginners, SQL is the first must-have skill. It’s the language used to query, filter, join, and shape data. If you’re serious about entering the field, SQL is hard to avoid.
Python also matters because it helps automate tasks, handle files, transform data, and build parts of pipelines. Some roles lean heavily on SQL. Others use both SQL and Python every week.
Besides programming, there are a few concept-heavy areas to learn:
- Data modeling: How tables and datasets should be structured
- System design: How different services and data flows connect
- Cloud: Platforms like AWS and Azure where data systems often run
At first, those topics can sound bigger than they really are. Once you break them into small pieces, they become much easier to learn.
ETL pipelines are the heart of the job
If there’s one concept to remember from an intro to data engineering, it’s ETL. ETL stands for Extract, Transform, Load.
- Extract means pulling data from a source
- Transform means cleaning it, reshaping it, or changing its format
- Load means sending it to the destination where it will be used
That destination might be a database, a data warehouse, a reporting tool, or another system.
Garzon describes this as a large part of a data engineer’s day-to-day work. They spend a lot of time “messing around with the data,” meaning they move it, clean it, standardize it, and make it ready for use.
The water analogy fits here too. Think about taking water from a source, treating it, and sending it through pipes to the right place in a home. ETL does the same thing for data.
The video description also mentions ELT, which stands for Extract, Load, Transform. The order changes, but the goal stays similar: move data from raw form to something useful.
If you’re building a beginner roadmap, start here:
- Learn SQL first
- Add Python basics
- Understand ETL and ELT
- Study data modeling
- Get familiar with cloud tools
That path won’t teach you everything, but it gives you the right base.
How data engineering differs from software engineering
Software engineers and data engineers both work with code, systems, and technical problems. Still, they usually solve different kinds of problems.
A software engineer might build the product you see. On YouTube, for example, that could mean the buttons, the video page, the layout, or the mobile app interface. A front-end engineer is one example of this work.
A data engineer works one layer behind that. Once the app exists and people start using it, the product begins generating data. Views, clicks, comments, watch time, likes, and user actions all create records. Then the data engineer steps in and builds the systems that extract, transform, and load that data.
So the cleanest distinction is this: software engineers create the systems that generate data, while data engineers make that data usable.
There can be overlap. Many tech roles share tools and concepts. In smaller teams, one person might even do parts of both jobs. Even so, the main focus stays different. Software engineering centers on building the application itself. Data engineering centers on the movement, structure, and quality of the data that application produces.
If you’re trying to choose between the two, the better fit often comes down to what you enjoy more, product features or data systems.
The biggest challenges data engineers face today
Data engineering keeps growing because businesses keep collecting more data, and more data creates more problems.
The first challenge is scale. Storing and processing large volumes of data costs money, takes planning, and gets harder over time. It’s not just about today’s data either. Companies also have years of historical data sitting in old systems, and that data still matters.
The second challenge is data quality. Bad data leads to bad decisions. If a manager looks at a dashboard and the numbers are wrong, the business may act on the wrong signal. That is why data engineers spend so much time validating data, checking for missing values, fixing broken pipelines, and confirming that fields mean what people think they mean.
Another major issue is the source of truth. In big companies, data often passes through many teams and systems before someone sees it in a dashboard. By the time it reaches decision-makers, people may not know which version is correct.
If a company can’t identify its source of truth, even a polished dashboard can mislead people.
That problem becomes more serious as organizations grow. A sales team may trust one report. Finance may trust another. Product might use a different dataset entirely. Data engineers help reduce that confusion by building cleaner, more reliable foundations.
Real-world examples of data engineering in action
Data engineering can sound abstract until you connect it to tools people use every day. Once you do that, the role becomes much easier to see.
Take Amex fraud alerts. If a card is used at an unusual time or in a suspicious pattern, a system may flag or block the transaction. Machine learning often powers that kind of detection, but the model still depends on data pipelines, clean transaction records, and reliable data movement. That foundation is data engineering.
Then there is Costco inventory. Retailers need to know what they have, what is selling, and what they may need next month. Item-level tracking, stock movement, and forecasting all depend on data being collected and organized correctly. Without strong data systems, inventory decisions become guesswork.
YouTube gives another good example. The platform can track watch time, clicks, saves, comments, and viewing patterns second by second. That data helps power reporting, recommendations, and platform decisions. Again, none of that works well unless the underlying data infrastructure works first.
These examples show why the field matters so much. Data engineering isn’t only about databases. It’s about making real business systems work, from fraud prevention to forecasting to recommendations.
How to break into data engineering
For many people, the smartest first step is not data engineering right away. Garzon recommends starting as a data analyst, especially if you’re new to tech.
Why starting as a data analyst makes sense
Breaking in as a data analyst can take about three to six months, depending on how much time you have. That path often requires fewer technical skills at the start, and in some cases you can get going with one main language.
This approach works because it gives you the foundation first. You learn how data is stored, how teams use it, and how to think about business questions. After that, moving toward data engineering, machine learning engineering, or data science becomes much more realistic.
For beginners, this is a practical route because it lowers the barrier to entry without closing off future options.
If you already work in tech or IT
If you come from IT or another technical background, the jump to data engineering can be more direct. Garzon says the answer is yes, you can become a data engineer, especially if you already understand systems, infrastructure, or technical troubleshooting.
After you move into the field, you can specialize in areas such as:
- Cloud, including AWS or Azure
- DevOps
- Solutions architecture
That flexibility is one reason data engineering appeals to experienced professionals. It opens several paths instead of locking you into one narrow role.
Where the career can lead next
The obvious next area is AI and machine learning. Garzon makes an important point here: machine learning isn’t brand-new. He references Jeff Bezos talking in 1999 about using machine learning for book recommendations at Amazon.
What has changed is speed. Companies can now process larger amounts of data much faster, often in near real time. That is why recommendations on platforms like Spotify can react quickly to what you just listened to.
He also mentions edge computing as another direction. While the video doesn’t go deep into it, the message is clear. Data engineering connects to a wide range of future-facing roles because data sits underneath all of them.
Resources that can help you get started
You don’t need one perfect resource. You need a useful mix of learning, practice, and repetition.
Garzon points to several places people often use:
- Books on data engineering fundamentals
- Online courses
- Reddit communities
- Stack Overflow
- SQL practice and training
If you want a simple place to begin, Data Engineer Academy’s free SQL training is a practical starting point. If you want a broader view of programs and learning paths, you can also explore Data Engineer Academy.
The main thing is to keep your learning tied to real skills. Learn the basics, practice often, and build from there.
FAQ about data engineering
What is data engineering?
Data engineering is the work of collecting, moving, cleaning, and organizing data so people and systems can use it. Data engineers build the pipelines and infrastructure behind reports, dashboards, machine learning systems, and business decisions. Their main job is to make raw data reliable, accessible, and useful.
How long does it take to get started in this field?
Based on Garzon’s overview, many beginners can break into data work as a data analyst in about three to six months, depending on available time and consistency. Moving into data engineering can take longer, but starting with analyst skills often gives people a faster and more realistic entry point.
Which tools should beginners learn first?
SQL is usually the best first skill because it sits at the center of querying and shaping data. After that, Python is a strong next step for automation and data handling. Once those basics feel solid, learn ETL, data modeling, cloud concepts, and system design in small pieces.
What is the difference between ETL and ELT?
ETL means Extract, Transform, Load. ELT means Extract, Load, Transform. Both approaches move data from one system to another and prepare it for use. The difference is the order of operations. In both cases, the goal stays the same, turn raw data into something trustworthy and usable.
Can someone from IT become a data engineer?
Yes, especially if they already understand technical systems, infrastructure, or problem-solving in a production environment. IT professionals often have skills that transfer well into data engineering. After making the move, they can also branch into cloud work, DevOps, or architecture-focused roles later on.
Conclusion
Data engineering is easier to understand once you stop thinking of it as a mystery role and start seeing it as infrastructure. It is the hidden system that helps data move, stay clean, and become useful.
That matters because every modern company runs on data in some way. If you want a practical first step, start with SQL, learn how pipelines work, and build from there. For a beginner-friendly place to start, Data Engineer Academy’s free SQL training is a straightforward next move.
