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Five Key Tools to Learn

· 3 min read
Parham Parvizi

You can search the web and get lost trying to find information about what tools data engineers use every day. Instead of guessing, let’s find out what tools the data engineers at use. Their data engineers work with major companies, so they must know what critical tools you need to learn to be a data engineer. Let’s take a look:

Python, Bash, SQL

These are basic programming languages. All three are great to learn, but the most critical of the three is Python. It’s the number one data engineering and data science programming language. The good news? It’s easy to learn. In fact, if you have about five hours open this weekend, check out this tutorial from You’ll be surprised by how fast you pick it up.

Docker, Kubernetes

These are container orchestration platforms that allow applications to remain elastic—letting them expand or shrink—based on the amount of data being accessed. All major Cloud vendors offer a Kubernetes (k8s) engine on their platform. These tools are especially useful for building cloud agnostic data pipelines and applications. The wide-spread use of docker and k8s is so prevalent that these vendors do not change its name on their cloud, unlike the other services listed below. Kubernetes is called Kubernetes on the cloud.


This is a data processing engine that allows apps to handle large amounts of data. It’s undisputedly the number one big data tool and has kept its seat on the throne for the past 7 to 8 years—which is technically a lifetime in tech. Spark helps data engineers scale up their pipelines to millions and billions of users. Spark is backed by a major Silicon Valley heavy hitting company called Databricks. Each cloud vender has a slightly different name for their Spark service: Google Dataproc, Azure Databricks, and AWS EMR


This is a platform for real-time data streaming and processing. Kafka is the glue that holds things together for data engineers. It’s like that shelf in your house where you put your keys, wallet, gloves, and mail. It’s the first place everyone puts things in and the place you go to when you want to grab things on your way out. Kafka helps data engineers manage incoming and outgoing data as a very large scale. Each Cloud vendor calls their Kafka service slightly differently again: Google Cloud Pub/Sub, Azure Event Hub, and AWS Kinesis.


This is a workflow orchestration platform that’s quickly become the number one tool in data engineering. Airflow is a python-based orchestration tool that helps data engineers build sophisticated data pipelines with multiple steps and dependencies. Airflow is backed by a promising startup called They offer managed Airflow services on Azure and AWS while Google Cloud provides their own Airflow service called Cloud Composer.

Here’s a tip: When you want to learn these tools, pick a company and learn to use their data on that cloud. That means if you want to learn how Apple uses Airflow, learn it on Apple’s cloud. If you want to learn how Google uses it, learn it on Google Cloud.

If you learn these tools, you will get a job.