The Importance of DevOps in Data Engineering

In this article, I would like to discuss the forefront of DevOps in Data Engineering and what benefits can be gained by teams.

Data Engineering is a crucial aspect of modern business, as it involves the design, construction, and maintenance of systems and services that extract, transform, and load data for data analysis and data driven decision making. They build ETL and ELT data pipelines and platforms such as Data Lakes, Warehouses or Lakehouses and are also responsible for data quality. Data Engineers provide the data to Data Scientists, Data Analysts and employees from business departments who work with (self-service) BI tools, for example.

One way of how Data Engineers can improve their productivity and the reliability of their systems is by embracing a DevOps mindset and adopting the associated tools and practices. In this article, I would like to explore the benefits of DevOps in Data Engineering and provide some examples of how it can be implemented.

What is DevOps?

First of all let's recap what DevOps exactly is: It is a culture, set of practices, and tools that aims to improve the collaboration and communication between Software Developers and IT operations professionals. It emphasizes the importance of automation and continuous delivery in order to speed up the software development process and reduce the risk of errors. The key principles of DevOps include [1]:

• Automating as much of the software development and deployment process as possible, including testing, deployment, and infrastructure management.
• Regularly releasing small updates to software rather than waiting for large, infrequent releases. This allows for faster feedback and iteration and thus provides continuous delivery.
• Encouraging collaboration and communication between developers and IT operations professionals throughout the software development process.
• Monitoring the performance and stability of systems in production and using that information to improve the development process.

Benefits of DevOps in Data Engineering

At the end of the day, Data Engineers are also developers and spend a large part of their work on the same tasks, also many have the background of a generalist Software Engineer. So the principles and approaches of DevOps can ultimately also be applied in Data Engineering. There are several benefits to using DevOps practices in Data Engineering, so that the above mentioned principles and resulting advantages can also be applied beneficially to Data Engineering. Here are some examples [1][2][3]:

• Increased efficiency: By automating tasks and enabling continuous delivery, Data Engineers can save time and reduce the risk of errors. This allows them to focus on more important tasks, such as analyzing data and building new features.

• Improved collaboration: DevOps emphasizes the importance of collaboration and communication between different teams, which can be particularly useful in Data Engineering where multiple teams may be involved in building and maintaining systems.
• Better quality: Automated testing and continuous delivery can help Data Engineers identify and fix issues faster, leading to higher quality systems.
• Enhanced security: Automated processes can also help Data Engineers ensure that security best practices are followed, such as encrypting data in transit and at rest.

Summary

DevOps has established itself over the years as a meaningful standard in Software Engineering and other disciplines. It therefore only makes sense to use this approach in Data Engineering teams as well, if possible. DevOps practices can be extremely useful for Data Engineer teams, helping them to build and maintain reliable and scalable pipelines and systems more efficiently. By automating tasks, enabling continuous delivery, and improving collaboration and monitoring, Data Engineers can improve the quality and security of their systems while also saving time and reducing the risk of errors.

Sources and Further Readings

[1] Atlassian, 5 Key DevOps principles (2023)

[2] XENONSTACK, DevOps Best Practices for Data Engineers (2022)

[3] NetApp, What Is DevOps? - Practices and Benefits Explained | NetApp (2023)