DevOps vs DataOps

What are the differences between software development and data development? In this chapter, we explore the similarities and differences between DataOps and DevOps through the specific challenges they are designed to solve.

DevOps Combines Development and Operations to Improve Quality, Efficiency, and Speed for Software Development

DevOps is a software development methodology that combines processes and practices to improve quality, efficiency, and speed in development, implementation, and delivery, while making software more reliable and ensuring that the maintenance and operations of the software are as agile as possible.

In other words, DevOps combines or unifies development and operations. It shifts the thinking from project to product and takes the entire lifecycle into consideration. In practice, this means introducing agile development methods, such as Scrum, and automating as much as possible, i.e., testing, version control, CI/CD, etc.

A crucial part of DevOps is measuring the development process and making it more efficient. This is achieved by removing obstacles and waste from processes and by continuously trying to improve the efficiency of the development teams. Being responsible also for operations and maintenance within the development team improves the quality of the software, as it is in the developers’ interest to make the software work well and require as little maintenance as possible.

One of the core things we should not forget in DevOps comes from agile methods: collaboration and communication. This means that there should be clear communication channels between all relevant stakeholders and a continuous feedback loop from end users to developers.

DataOps Involves Holistic Responsibility for the Development and Operations of Data Products

To understand how DataOps differs from DevOps, we need to understand how data development differs from software development. In a way, you could simplify it like this:

Business applications are about automating and improving business processes and functions. Data development is about deriving insights from the data created by said business processes and applications.

One of the most important differences between data development and software development is the complexity that comes with working with data. Data changes continuously and can come from a wide range of different sources, making it a challenging task to develop, manage, and maintain data pipelines and products. In addition, data development is downstream and dependent on the underlying business applications, which adds an extra layer of complexity.