Why big data needs DevOps?

The process of extracting accurate and meaningful insights from big data is tough. And it becomes even more challenging with a lack of coordination between big data software developers and IT operations, which is prevelant in most enterprises. Even though IT organizations often practice sound DevOps strategies for other supported applications, big data projects remain in siloes for a variety of reasons.

Big data science is a complex concept, especially the analytical sciences portion of big data, which has steered several IT leaders to abandon the DevOps processes and procedures completely. The field of big data is relatively new and its working is still foreign to many IT professionals. Therefore, analysts and big data developers have formed their own group apart from the operations department of the business. This separation of functions is how many big data organizations operate to this day.

In the following blog post, we will answer two questions.

Why big data needs DevOps?

Due to this separation, the same bottlenecks and inefficiencies that were solved with DevOps practices in other applications, applications are now showing up in big data projects. Moreover, since some big data projects are more challenging than originally expected, many IT leaders are now under increased pressure to produce results. This has forced analytics scientists to revamp their algorithms. Such major changes in analytic models often require drastically different resources and resources and infrastructure than what was originally planned for. Yet, the operations team is kept out of the loop until the last minute without any proper collaboration. This slowdown affects potential competitive advantage that big data analytics can provide, and this is precisely why DevOps is needed.

How does DevOps help Big data projects?

Previously, while building an enterprise-grade application, multiple software development teams would work separately on the components of the application. When all the independent building and testing was done, the pieces were combined and tested together. This process would occur multiple times, but nowadays, those kindkinds of timeframes simply aren’t tenable. Today’s market mandates faster business innovation, faster development of new products, and faster response to changes in the market.

An Agile environment certainly facilitates adaptive environment and promotes evolutionary development. Agile development is closely related to DevOps, which provides evolving integration between the software developers who build and test applications. And because of this agility, enterprises are now considering of moving their Big Ddata and Hadoop projects to public cloud services for gaining the much-needed agility they need for their data scientists.

With a scalable and flexible infrastructure platform, IT organizations and development team together can spin up virtual Hadoop or Spark clusters within minutes.

The advantages of mating big data with DevOps far outweigh any integration challenges. The efficiencies and coordination benefits also help in streamlining processes that helps in speeding up the ability to make analytical changes on the fly, thereby getting more out of the data being mined