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Data engineering is a critical job at a growing number of companies. Data engineers must construct and maintain vast repositories of data crucial to business operations; data scientists and data analysts depend on this work to find the right data and perform effective analyses. For an organization of any size, data engineer skills are critical for long-term survival.

If you’re ready to become a data engineer, there are crucial things to keep in mind—and core skills to learn. For starters, you need to understand the many specialties that fall under the bigger umbrella of data engineering. Some are more IT-focused, such as managing and running database systems distributed throughout a cloud platform. Others are more developer-oriented, with a focus on writing applications that integrate data.

Data engineers may also assist data scientists in retrieving, analyzing, and even presenting data. With that in mind, let’s break down the skills necessary to become an effective data engineer.  

Learning a Programming Language

First and foremost, you must become proficient in at least one programming language. For data engineering, the three most common are:

Python is used most often. As such, you will want to learn many of the computation and data libraries available to Python, such as:

Data Querying

As a data engineer, you need to fundamentally understand how data is stored and managed, whether it’s hundreds of pieces of data… or billions. Start small. Learn how to store data in a small MySQL database on your own computer; how to manage the data and its indexes; how to query the data in SQL; and how to create views and stored procedures. Learn how to group and total data, such as sums, averages, and so on.

Then, learn how to do the same in a NoSQL database such as MongoDB. MongoDB uses a completely different way of storing data compared to MySQL. Learn the difference between relational tables that MySQL uses and collections that MongoDB uses. Learn how to transform data with aggregations.

For both systems, you’ll want to at least be familiar with IT concepts such as what replica sets are and how data is sharded. As a data engineer, you might not be the one managing this (depending on your team and company), but it’s good to have some idea of what’s going on.

Then move up to bigger systems geared towards “Big Data.” Examples here include Google Datastore and AWS DynamoDB.

Data Analysis and Statistics

Data engineering goes far beyond simple queries. Whether you’ve become comfortable doing select statements and joins in a database, you need to have a much deeper understanding of statistics and different types of data analysis. You’ll want to study sites on data analysis and statistics, and even consider purchasing a book on it.

IDE and Platforms

For data engineers at every step of their careers, becoming comfortable with tools and IDEs is critical. One important tool is Jupyter Notebook, which is popular among data engineers who use Python. Jupyter Notebook lets you run Python code interactively and have charts and diagrams appear right alongside the code. (It’s much more than that, but that’s a brief description.)

Data Visualization

Data visualization is essential for data engineers, especially those who work with data scientists on generating analyses, results, and presentations. People need to see the information you’re presenting, and you want it in a way they can easily digest. That includes charts, graphs, tables, and so on.

First, you’ll want to learn different types of charts and graphs, such as scatter plots, normal distributions, histograms, density plots, and so on. Most of these graphs and charts are produced by libraries; for Python, that means learning how to use the math-plotting library called Matplotlib.

Other Tools and Concepts

As you progress in your education, you’ll eventually want to explore more advanced topics. These aren’t required for landing an entry-level job; instead, these are topics typical of more advanced positions:

Hadoop: This set of tools lets you manage files across multiple physical computers. It provides the foundation for a Big Data system.

Spark: This data analytics engine works well with Hadoop.

Hive: This data warehousing engine, built on top of Hadoop, is meant for managing huge amounts of data scattered across multiple physical computers and drives.

ETL: This is a concept, not a tool. Data engineers often manage data by extracting large volumes of data from multiple sources, then combining it, manipulating it, and transforming it; then the resulting set of data derived from the first is loaded into a different database. This process is known as an Extract, Transform, and Load pipeline.

MapReduce: Similar to ETL, this is a concept where you read data from several sources, and “reduce” it down to a smaller set of data.

Amazon Web Services EMR: This is AWS’s set of tools for managing the above, including Hadoop, Spark, and Hive. (Be careful practicing with this one, as it can get very expensive because you’re creating clusters, which means you’re creating several servers!)

Conclusion

Becoming a data engineer is no small feat. It requires a lot of training. But people who work in data engineering tend to really enjoy their jobs. When it comes to acquiring new skills, take it slowly; have patience and practice. There will always be something new to learn.

 

Related Data Engineer Jobs Resources:

Big Data Engineer Interview Questions

How to Become a Data Engineer