Dataops Leveraging

DataOps: Leveraging DevOps Principles for Better Data Analytics

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If you’re a developer, software tester or IT Ops admin, you probably know all about DevOps. But what if you’re a data analyst? Do you feel left out of the DevOps revolution?

If so, you have not yet heard of DataOps. DataOps an approach to data analytics and storage that allows data engineers to benefit from the same principles and philosophies that the DevOps movement emphasizes.

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Bringing DevOps to data analytics

When you combine DevOps with data analytics, you get DataOps.

DataOps is the extension of DevOps values and practices into the data analytics world. The DevOps philosophy emphasizes seamless collaboration between developers, quality assurance teams and IT Ops admins. DataOps does the same for the admins and engineers who store data, analyze data, archive data and deliver data.

In other words, DataOps is all about streamlining the processes involved in storing, interpreting and deriving value from big data. It aims to break down the siloes that have traditionally separated different teams from one another in the data storage and analytics fields.

With better communication and collaboration between the different teams come faster results and better time-to-value. DataOps is a way to optimize your data analytics and storage workflows in the same way that DevOps does for application development.

DataOps infrastructure

Adopting DevOps requires some changes to infrastructure. To make the most of DevOps, you’ll want to migrate to a microservices-based workflow, which takes advantage of containers and other agile technologies.

DataOps also requires admins and engineers to leverage next-generation data technologies when building their data storage and analytics infrastructure. They need data processing solutions that are scalable and highly available — think cluster-based, redundant storage.

DataOps infrastructure also needs to be able to accommodate diverse workloads, in order to achieve the same agility as a DevOps delivery pipeline. Building a data processing toolset composed of diverse solutions — from log aggregators like Splunk and Sumo Logic and big data analytics tools like Hadoop and Spark — is key to achieving that agility.

Conclusion

Just as DevOps is revolutionizing the way software is designed, built, tested and delivered, DataOps can and will do the same for data storage and analytics. It will make work easier for data professionals, and lead to better value for organizations storing and processing large volumes of data.

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Cordny Nederkoorn is a software test engineer with over 10 years experience in finance, e-commerce and web development. He is also the founder of TestingSaaS, a social network about researching cloud applications with a focus on forensics, software testing and security.


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