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- 6 minutes read’s New Learning Path: Data Engineering

Calling all future data engineers! Would you like to learn to use SQL? Then our new Data Engineering Learning Path is just for you!

We’re very excited to announce the release of a new learning path at Data Engineering, published in July 2020. This is the second learning path offered in our platform. So far, we offered an analytical path, SQL Querying & Reporting, that focuses on writing queries and business reports in SQL.

Our learning paths are designed to give you a comprehensive understanding of how to use SQL in a particular field or profession. So let’s talk about what data engineering is, what skills it requires, and why SQL is essential.

What Is Data Engineering?

Data engineering is a relatively new IT field that began with the arrival of Big Data. The volume of data collected and processed all over the world is getting larger and larger. Websites, smartphones, apps, social media sites, and video streaming platforms generate a constant stream of data. It’s estimated that over 2.5 quintillion bytes of data are created every single day. Big Data technologies are what process these vast amounts of information. Creating and maintaining the infrastructure to host, serve, or process large quantities of data is the core of data engineering.

What’s a data engineer?

A data engineer is an IT professional who sets up computing and storage infrastructures that process large amounts of data. Being a data engineer combines a lot of skills. You must know:

  • Data structures and algorithms.
  • Different data storage technologies.
  • Java, Python, and possibly other programming languages.
  • How to transform data from one format to another.
  • Distributed data infrastructure and maintenance.
  • Data modeling and setup in different data storage engines (polyglot persistence).
  • Distributed and Cloud computing systems (e.g. AWS, Google Cloud Platform).

Databases and SQL Are Data Engineering Fundamentals

What does all this have to do with SQL? Well, everything! SQL and database knowledge are fundamental to data engineering. SQL is the universal language of data: it’s used in relational databases and in many other data processing technologies.

Data engineers must also know how to create data structures for further analysis. They also need to understand how to create data pipelines and transform different types of data into usable forms. And they have to do all this in the most efficient way. Our Data Engineering Path covers all of this from a relational database point of view, which is the most popular database in data engineering.

The Data Engineering Path at

It’s best to start your career in data engineering by learning the basics: how to structure and work with relational databases. Relational databases are the foundation of all data systems. Our Data Engineering Path teaches you how to work with relational databases from the perspective of a person who sets them up. We discuss how to create the internal structure of a database: how to create database objects (tables, views, indexes), how to remove them, and how and when to modify them.

So far, our Data Engineering Path contains a single learning track: Creating Database Structure. (More courses will be coming!) This track will teach you the basics of creating tables, views, and indexes in a relational database. There are five courses in this track:

  1. The Basics of Creating Tables in SQL –Everything starts here. This course covers the basic syntax of creating tables in relational databases. You’ll learn the CREATE TABLE and DROP TABLE syntax, what primary and foreign keys are, and SQL’s fundamental data types.
  2. Data Types – In this course, you will discover more SQL data types. We discuss the text, numeric, and Boolean data types introduced by the SQL standard. We show different ways to auto-generate primary key values, i.e. IDENTITY columns and sequences. We also show how to modify columns’ data types and when you should do it. It is extremely important to know these concepts; without this knowledge, you can’t call yourself a data engineer.
  3. Constraints – Next, we cover different constraints in a SQL database. A constraint is a rule that the data must follow. We discuss the NOT NULL, UNIQUE, DEFAULT, and CHECK constraints. Finally, we show you how to modify or remove an existing constraint.
  4. Views – SQL views are “saved” SQL queries. A view can be queried in the same way as a table. You will learn how to create, modify, and remove views. We also discuss updateable and materialized views.
  5. Indexes – An index is a data structure used to query data efficiently. You’ll see how indexes are built internally, how to create an index, and how SQL uses indexes to run your queries.

Where’s the Data Engineering Learning Path on

You can find the Data Engineering Path at the bottom of the main page, below the Analytical Path.

If you want to start your Data Engineering journey with programming, check our Python courses at our sister site, Vertabelo Academy.

Who Should Take the Data Engineering Learning Path?

Data engineering is a fairly new field. Data engineers are not produced by universities; rather, they are born from experience and have transitioned into this career from other roles.

We recommend our Data Engineering Learning Path to programmers interested in becoming data engineers. If you’re a software engineer, programmer, or software developer, you already know programming very well. The missing piece of the puzzle is learning the “data” part, i.e. how to set up the data infrastructure. With our Creating Database Structure track, you’ll learn the foundations of working with relational databases.

Our Data Engineering Path is also useful to data scientists who want to transition into a more technical role. Data scientists understand the business value of data. To move into data engineering, they need to learn the technical side of data processing. If you’re working in a smaller company, your role might combine data science and data engineering.

Even if you don’t want to become a data engineer, getting familiar with data engineering topics will make your collaboration with data engineers easier. You’ll better understand how they think, how to talk to them, and how to pitch your ideas and solutions to them.

Finally, we also recommend this path to Computer Science students interested in data processing.

Upcoming Additions to the Data Engineering Path

The Creating Database Structure track is just the beginning of our Data Engineering Path. We’l be adding courses that cover writing stored procedures, user defined functions and triggers, programming ETL processes, and other topics. Start learning today and stay tuned for more!

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