The Perfect Roadmap for Data Engineering 2024
Master the Fundamentals
Before I began my journey in the data engineering space, I scouted for the best possible roadmap. Throughout my research process, I never came up with two roadmaps with the same plan. This created more confusion as I wandered for months searching for the ideal route to my dream career.
This speaks a lot about data engineering—it is a broad field! The authors of the many videos you have seen have almost different roadmaps to data engineering. Most of the time, this stems from the tools they were introduced to when they began their career.
In a bid to satisfy my curiosity and craft a perfect plan for my journey, I came across this tweet:
Here was another thing I found:
These and many more I dug out and drew my plan!
Here’s all you need
The thing is, there are tons of tools and many programming languages in the space of data engineering, and you may end up not becoming a data engineer if you decide to gain mastery of all of them before your first role.
SQL and Python take center stage in the screenshots provided above. They are the backbone of data engineering.
Now, let’s do some analysis of these screenshots.
For the first screenshot:
- SQL is the primary language for querying data in Snowflake, BigQuery, and Databricks.
- SQL is a fundamental language for data modeling in relational databases and graphical representations such as ERDs.
- Python is the primary language for defining and configuring workflows in Apache Airflow.
For the second screenshot, for Pyspark, all you need is Python.
By now, it should be evident that mastering Python and SQL is all that is required. When you have a good mastery of them, it’ll be very easy to utilize other tools.
For other tools in data engineering that may not be necessary starting off, it is important to have a surface knowledge of them.
This means knowing what they are used for and the stage in the data engineering process when they are required. The goal is that they shouldn’t sound strange to you when mentioned.
Why SQL and Python?
Apart from the reasons stated above, here are a few other reasons I consider SQL and Python for a junior role in data engineering.
- Among the roadmaps you have come across, the point of intersection lies in SQL and Python. This means, for sure, you need them. There’s no certainty with the rest of the tools, starting off. With good mastery of SQL and Python, you can build amazing projects that can make you vulnerable to good opportunities and continue the learning process.
- It is also important to note that not all companies are the same. What do I mean? The use of data engineering tools varies among companies. The set of tools company A uses may differ from that of companies B and C for various reasons. Not all companies use Hadoop or Sqoop, for example. The choice of technologies depends on specific project requirements, company preferences, and the evolving landscape of data engineering tools. You may not be certain what tools your dream company uses, but you can be sure there’ll be a need for SQL and Python skills.
- There’s always room for learning. The professional data engineers you see today once didn’t have it all. Some went in with SQL skills, and some with Python. This tells you that you can always learn and level up! Some companies go as far as getting courses for their employees and having them level up to their standards.
Final thoughts
The perfect journey into data engineering necessitates a solid foundation in SQL and Python.
Mastering SQL provides the foundational skills to interact with relational databases, enabling data engineers to design, query, and manipulate data effectively. Meanwhile, Python stands as the versatile Swiss Army knife, empowering engineers with the ability to script, automate, and interface with a myriad of data tools and systems.
In the era of data-driven decision-making, these two languages converge to form the cornerstone of a data engineer’s toolkit.
From crafting intricate ETL pipelines to modeling complex data structures, the synergy between SQL and Python is the catalyst for success in the rapidly evolving data engineering landscape.
As an aspiring data engineer, embark on a journey equipped with a dual proficiency in SQL and Python. This not only opens the doors to a multitude of opportunities but also ensures adaptability to emerging technologies and methodologies.
With this strategic roadmap, data engineers can confidently traverse the data engineering landscape in 2024 and beyond, contributing to the seamless integration, transformation, and analysis of data at every juncture.
