Data engineering is one of the hottest career tracks out there. According to Interview Query’s 2021 Data Science Interview Report, data engineering interviews increased by 40%in 2020.

A data engineer’s job is to make raw data accessible to other professionals working with data by cleaning and transforming data as needed. They do so through data engineering tools such as Cadence and Prefect, which we’ll cover in this article.

Read on to learn about five essential data engineering tools every engineer should know. You’ll also learn how you can become a data engineer without a bachelor’s or master’s degree in computer science.

5 Essential Data Engineering Tools Every Data Engineer Should Know

If you’re new to data engineering, there are many online resources you can start looking at to gain a better idea of how to become a proficient data engineer. To begin, you’ll have to start learning about the most popular and powerful tools in the industry.

Below, we highlight the top five essential data engineering tools for 2021. After learning about how to use these tools, you’ll be able to start working with online resources such as Kaggle, where you can find free resources and datasets about a wide range of topics, from Bitcoin to traffic signs to stroke occurrences. 

Here are five data engineering tools every data engineer should know about and try out.

1. Cadence

Cadence is a popular code platform that makes coding a lot easier for you.

It’s fault-tolerant, which means you can write stateful applications without having to worry about handling complex process failures. This will save you the stress of thinking about the durability, availability, and scalability of your application.

Cadence will help you learn:

  • How to use and program MySQL/Postgres storages
  • How to sharpen your Go, Java, Python, and Ruby skills
  • Capacity planning skills, since Cadence allows for horizontal scaling
  • How to develop distributed applications

2. Prefect

Prefect is an easy-to-use open-source data pipeline manager you can use to automate data and build data pipelines. In addition to offering a cloud-based infrastructure, it has a private infrastructure you can use to test and run your code.

Prefect will help you:

  • Sharpen your Python skills, since Prefect’s framework is based on Python
  • Grow your ability to build, organize, and manage data pipelines, which is a crucial skill for data engineers
  • Build tasks and data workflows

3. Great Expectations

Great Expectations is a Python library that you can use to validate, document, and profile your data. This will help you improve communication about data with others in addition to helping you maintain the quality of data.

It integrates with DAG execution tools such as Kedro, Dagster, Prefect, and Airflow. 

Note that Great Expectations doesn’t store data. Instead, you should use it to learn more about metadata and communicating between teams. Great Expectations produces readable documents based on expectations, or assertions about your data, which function as data quality reports.

Great Expectations will help you:

  • Learn how to organize and document data assets
  • Improve your ability to create top-notch data quality reports and data documentation
  • Sharpen your Python skills, since Great Expectations is Python-based

4. Amundsen

Amundsen started at Lyft and offers data and metadata discovery solutions. It comes with a variety of tools that help data engineers become more productive, such as its metadata and data builder services.

It also comes with a powerful search engine and a library, which holds common codes for microservices in Amundsen.

Amundsen will help you:

  • Develop your ability to process and work with metadata
  • Sharpen your Python skills
  • Learn how to use Apache Airflow, since Amundsen can be integrated with Airflow
  • Learn about how you can interact with data in a more efficient manner

5. Marquez

Developed by WeWork, Marquez focuses on data lineage and quality. Like Great Expectations, it can teach you a lot about data health and governance and has a catalog of jobs as well as datasets.

Marquez stands out by giving you an in-depth look at metadata and helping you understand what a healthy data ecosystem looks like. 

Using Marquez will help you:

  • Develop your ability to process and work with metadata
  • Sharpen your Java and Python skills
  • Learn how to use Apache Airflow, since Marquez can be integrated with Airflow
  • Learn how you can build trust in data by utilizing and managing metadata and contribute to a self-service data culture

Ready to switch careers to data engineering?

Data engineering is currently one of tech’s fastest-growing sectors. Data engineers enjoy high job satisfaction, varied creative challenges, and a chance to work with ever-evolving technologies. Springboard now offers a comprehensive data engineering bootcamp.

You’ll work with a one-on-one mentor to learn key aspects of data engineering, including designing, building, and maintaining scalable data pipelines, working with the ETL framework, and learning key data engineering tools like MapReduce, Apache Hadoop, and Spark. You’ll also complete two capstone projects focused on real-world data engineering problems that you can showcase in job interviews.

Check out Springboard’s Data Engineering Career Track to see if you qualify.