7 Things Every Data Engineer Should Know

People generate massive amounts of data every day. To get insights from data, organizations need to capture and process them efficiently. That is when data engineers are called up. In this article, I’ll discuss the data engineering role and the skill set necessary to succeed in the role.

As the world generates more and more data every year, the IT industry creates new roles to deal with it. These roles include data analysts, data scientists, machine learning engineers, and data engineers.

You can read more about data engineering vs. data science, artificial intelligence, and machine learning here. In this article, I’d like to focus on data engineering and the corresponding skills set.

What Is Data Engineering?

Data engineers create and maintain the infrastructure necessary to store and process large amounts of data. Among other things, their responsibilities include:

• Identifying the kind of data that can be acquired.
• Ensuring that the data collection process meets the business requirements and industry standards.
• Defining database structures.
• Creating data pipelines and flows to ensure efficient processing of large amounts of data.

Data engineers are in high demand. This is naturally reflected in their paychecks. Now, let’s see what the employers’ expectations are with regards to their qualifications.

What Should You Know as a Data Engineer?

To be a successful data engineer, you need to master several programming languages and be very familiar with distributed computing, cloud data warehousing, and other tools related to processing large amounts of data.

1. SQL

SQL, or Structured Query Language, is an industry standard for communicating with relational databases, while the relational database is one of the standards for storing large amounts of business data.

In relational databases, data is stored in tables that are related to each other through common fields. For example, Uber might have a table with drivers, a table with customers, and a table with rides. The rides table is likely to reference the driver's ID and the customer’s ID in their respective tables. These connections allow you to pull information from different tables efficiently. For example, with one simple query, you can pull information on all drivers who have given a ride to a certain customer.

Relational databases are great at storing information about many other things. They house data about social media users with their preferences and activities. Records of customers with their order history, responses to marketing campaigns, and participation in loyalty programs are often in relational databases. Information about different stores with their respective stock levels and sales history is often stored in relational databases. These are just a few examples.

With so many organizations using relational databases, it is no wonder those who know how to build a well-structured database and how to interact with it efficiently are in high demand. As SQL is considered an industry standard, interacting with relational databases implies knowing SQL. The Stack Overflow Annual Developer Survey 2021 shows SQL among the top programming languages, with 47% of responders using it.

Source: Stack Overflow Annual Developer Survey 2021

Note that this survey includes all developers. When you look into developers who focus on data, the prevalence of SQL becomes even more apparent.

If you want to join other professionals who manage data efficiently with SQL, I recommend taking the Creating Database Structure learning track that focuses specifically on SQL for data engineers. With five interactive courses, you’ll better understand the technical side of data storage and learn the syntax used to create, modify, and remove tables, views, and indexes.

2. Python

The popularity of Python has skyrocketed in the last few years as demonstrated by the Stack Overflow Annual Developer Survey 2021. It even made it to the top 3 programming languages used by professional developers.

A great deal of its popularity comes from its prevalence in the data science and artificial intelligence (AI) fields. Self-driving cars, deep fakes, machine translation, and other AI applications are all driven by machine learning models written in Python.

This programming language has revolutionized data analysis, statistical modeling, and data visualizations. Its simple syntax and profound efficiency make Python a favorite programming language of researchers, machine learning engineers, data analysts, data scientists, and anyone wishing to automate their daily work.

No wonder Python is also one of the key tools of data engineers whose work is focused on data. They often use Python to create effective data pipelines and prepare data for future analysis and modeling.

If you want to master Python, I recommend LearnPython.com’s interactive courses, and specifically, the Data Processing with Python learning track.

3. Apache Spark

When the data gets really big, data engineers use Apache Spark. This is an open-source framework for developing data-processing pipelines. Apache Spark can assist data engineers with transforming huge amounts of data efficiently by distributing this process across multiple machines in a cluster.

If there is no need for multiple machines, Spark applications can also run efficiently on a single node without any cluster infrastructure. This adds flexibility, allowing you to use Spark even when you are working on smaller projects with not-so-huge amounts of data while still enjoying the benefits of Spark.

In addition to its efficiency and flexibility, Apache Spark is easy to use – it can be accessed interactively from the Scala, Python, R, and SQL shells. Moreover, it allows combining SQL, streaming, and complex analytics seamlessly in the same application.

4. Apache Kafka

Data engineers use Apache Kafka to capture real-time data through event streaming. What does this mean?

In traditional databases, data is usually viewed as a collection of values about certain objects like customers, products, orders, etc. If there are any changes related to collected values, we can simply update our database to reflect these changes (e.g., updating a customer’s email address or changing the stock quantity for a certain product).

However, not everything data engineers process is data of this kind. With the unprecedented level of user activity in the online world, organizations have become interested in collecting and processing information on these activities. These activities are a stream of events, which basically come in the form of log files scaled to millions or even billions of records.

Let’s imagine you have an application with 1 million daily users. You want to record the activity of each user – clicking, hovering, moving, etc. – which results in millions of user action events every hour. You want to access these records. You don’t need to change them; the events are immutable and therefore can be processed more efficiently. With its agility and responsiveness, Apache Kafka is one of the leading tools for processing event streams.

5. Apache Hadoop

Apache Hadoop is an open-source framework to deal with Big Data. It is not a single platform but rather a combination of modules that support distributed processing of large datasets across clusters of computers:

• Hadoop Distributed File System (HDFS) provides high-throughput access to application data.
• Hadoop YARN is responsible for job scheduling and cluster resource management.
• Hadoop MapReduce enables parallel processing of large datasets.

Although it is one of the most powerful tools in Big Data, Hadoop has some drawbacks, including the slow processing speed and the need for a lot of coding. Still, it is widely used by data practitioners for reliable and scalable distributed computing.

6. Amazon Redshift

For data analysis, you usually need a long-range view of data over time. This is often stored in a cloud data warehouse. Amazon Redshift is one of the leading data warehousing applications thanks to its speed, scalability, and security.

With Amazon Redshift, you can query and combine exabytes of data using standard SQL then leverage it in business intelligence, real-time streaming analytics, and machine learning models.

Familiarity with data warehousing applications such as Amazon Redshift is usually a required qualification in data engineering job descriptions.

7. Snowflake

Snowflake is similar to Amazon Redshift in that it offers a cloud-based data storage and analytics service. Compared to Redshift, it lacks integration with Amazon's rich suite of cloud services, of course. But it also enjoys some advantages:

• Snowflake offers instant scaling, while Amazon Redshift may take minutes to add nodes.
• Snowflake has more automated maintenance.
• Snowflake has better support for JSON-based functions and queries.

Snowflake team claims that with their tool, data engineers don’t need to spend time on managing infrastructure, planning capacity, and handling concurrency. Snowflake takes care of everything. The popularity of this tool among data practitioners supports these claims.

How to Get the Necessary Skill Set

Being a data engineer requires you to combine a lot of skills: a deep understanding of data structures, knowledge of different data storage technologies, familiarity with distributed and cloud computing systems, etc. Among all these skills, SQL and database knowledge are fundamental to data engineering.

So, if you are considering taking on a data engineer role one day, I recommend you start with the Data Engineering learning path at LearnSQL.com. It’s not enough for data engineers to know how to query relational databases; they should also know how to set them up.

That’s exactly what this learning path teaches you. You’ll start with the Creating Database Structure track that includes five interactive courses. These courses cover the basics of creating tables in SQL, the data types in SQL, the SQL constraints, and working with views and indexes. Join the track – the 336 coding challenges are waiting for you!

Bonus! Here are the top five books for aspiring data engineers.