DataOps tools – understanding and selecting the right stack and approach for your team

When it comes to building a successful DataOps team, one of the crucial factors to consider is the tools and technology used. But with a plethora of options available, how do you decide which tools are the best fit for your organization?

When building your DataOps tool capabilities, there are multiple factors to analyze: Skills, experience, and preferences of the team, requirements of your organization, cost, and – most importantly – what you are trying to achieve!

In this blog post, we have a closer look at how the DataOps tool stack differs from DevOps tools. We also give guidelines on choosing the correct tools.

One tool stack does not fit all

Tools used in DevOps are commonly found in DataOps as well. We have a stack of tools in DataOps that include version control, CI/CD pipelines, automated infrastructure configuration, ETL/ELT tools, orchestration, monitoring, data lineage, and data modeling tools, etc. Choosing the correct tools for your DataOps team should be based on the needs of your organization and level of experience of the people, and what is expected from them. 

There is no singular all-encompassing answer to the tooling. Some of the complexity of DataOps comes from the tooling. There is quite a variety of different kinds of technological solutions to organize the stack to help your DataOps team to succeed. 

There is a huge amount of open-source software you can leverage to most parts of your DataOps tool stack. Your team could even develop some of the needed tools by themselves with a reasonable effort, depending on their experience and skills. Neatly packaged SaaS/PaaS tools and platforms are also available that will combine most of the required tool stack. The real question behind the tooling is: Buy versus build?

In order to answer the question, we need to dive a bit deeper into the differences in the development and operations of software and data.

Leveraging the right mix of software and data warehouse development skills for optimal performance

Tool stack has some variation between DevOps and DataOps, but the bigger difference comes from the skill set differences and experience between software developers and data warehouse developers.

Software developers usually have much broader experience working with and developing the tool stack. It takes time to set up different environments, CI/CD pipelines, etc. The time to set up these naturally vary based on experience working with the chosen tools and working with constantly changing data creates problems of its own in configuring CI/CD for example. It also takes time to master all these tools and to get the development process performing. 

You should keep in mind that the tools are only one part of the DataOps methodology. The more tools you have, the more complexity it creates, and the more time it takes the team to master these. People with data management or warehousing backgrounds tend to have less experience with these tools. Usually, you need both software engineering and data warehouse development skills in your team, and there is demand for a solution that enables a smooth development process no matter which background the developer comes from.

Choosing and maintaining the right tools for DataOps

We can't forget the operations, either. When a business application fails, there might be various reasons behind it, like malfunctioning hardware, incorrect software configuration, insufficient resources, human error, bad design, or problems with security. When data pipelines – or whatever kind of data products you are building – fail, the reason might be any of the above. In addition, failures or changes in the business applications can also be the root cause, as the data platform is downstream and dependent on them.

If you built your DataOps tool stack, you must maintain it yourself in addition to the data platform. If you leverage SaaS/PaaS services on your stack, you outsource the responsibility to the service provider. This, of course, comes with a (subscription) price, but then again, is the engineering work free? If you have multiple DevOps and DataOps teams, then it might pay off to have a centralized team responsible for the tools, but as we are writing this (beginning of 2023), it seems that the demand for people on the market with expertise in DevOps tooling is much higher than the availability.

Either way, there is no definitive answer to this question. It all depends on the people, the complexity of your environment, budget, scalability, and the team’s focus, and of course, this all should be aligned with your enterprise architecture.