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DataOps is NOT Just DevOps for Data

04-04-2019  0 Comment(s)

One common misconception about DataOps is that it is just DevOps applied to data analytics. While a little semantically misleading, the name “DataOps” has one positive attribute. It communicates that data analytics can achieve what software development attained with DevOps. That is to say, DataOps can yield an order of magnitude improvement in quality and cycle time when data teams utilize new tools and methodologies. The specific ways that DataOps achieves these gains reflect the unique people, processes and tools characteristic of data teams (versus software development teams using DevOps). Here’s our in-depth take on both the pronounced and subtle differences between DataOps and DevOps.

 

The Intellectual Heritage of DataOps

DevOps is an approach to software development that accelerates the build lifecycle (formerly known as release engineering) using automation. DevOps focuses on continuous integration and continuous delivery of software by leveraging on-demand IT resources (infrastructure as code) and by automating integration, test and deployment of code. This merging of software development and IT operations (“DEVelopment” and “OPerationS”) reduces time to deployment, decreases time to market, minimizes defects, and shortens the time required to resolve issues.

Using DevOps, leading companies have been able to reduce their software release cycle time from months to (literally) seconds. This has enabled them 

to grow and lead in fast-paced, emerging markets. Companies like Google, Amazon and many others now release software many times per day. By improving the quality and cycle time of code releases, DevOps deserves a lot of credit for these companies’ success.

Optimizing code builds and delivery is only one piece of the larger puzzle for data analytics. DataOps seeks to reduce the end-to-end cycle time of data analytics, from the origin of ideas to the literal creation of charts, graphs and models that create value. The data lifecycle relies upon people in addition to tools. For DataOps to be effective, it must manage collaboration and innovation. To this end, DataOps introduces Agile Development into data analytics so that data teams and users work together more efficiently and 

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