The term DevOps references the combined efforts of software developers and information technology operations to speed up how software is developed while still delivering fixes, updates and new product features. The ultimate goal is to build a system that creates the necessary tools based on how the business is growing or changing.

Big Data refers to the huge data piles most organizations have collected and encourages teams to find new and helpful ways to leverage the data to make better products or services.  

Applying DevOps concepts to Big Data can unlock great insights for an organization and help them act on exciting opportunities. Collaboration between developers creating code and analyst who understand algorithms can help provide valuable operational perspective to business leaders. But, marrying the two does present some unique challenges.

The Challenge of Coordinating the Dev with the Op

DevOps operations is a growing movement that essentially combine the activities of developers with that of operational staff.

No longer are software developers just coding and leaving the implementation or delivery to other team members. Likewise, the operational workers aren’t just waiting until the code is written. Program development now sees a collaborative effort from tech minds on both ends of the spectrum.

But the challenges are abundant – why? The specific reason is because many organizations with a large team of both software developers and IT operations staff tend to be huge and coordinating efforts can be challenging. Additionally, analyzing data can be a new skill and asking the wrong questions or pulling the wrong data can lead to an incorrect answer.

Big data means big projects – let the team come together for a more cohesive workflow.

Automate the Process, Produce Better Products

Producing a superior product isn’t just about having the right talent – in many cases, it is about having the right platform for talents to use. This is especially true for big data projects where there can be huge amounts of data.

In order to release software faster and cheaper, more big data companies are now looking to automate their DevOps projects.

For example, cloud platforms designed to support the development of major technologies in all stages of the software lifecycle can support massive workforces, even if they’re distributed. From management of core files to streamlining work under a single system, cloud platforms do a lot. Automating the data going in can help get code built reliability and deployed in a consistent manner.

Workers who are accustomed to using different systems and handling different parts of the product lifecycle can come together more seamlessly on a platform designed to help them all work together.  Pairing massive cloud computing with machine learning, bigger, more complicated computations can be done quickly and accurately.

Cloud automation makes DevOps environments more efficient, with the only challenge being understanding what to automate.

How the DevOps Environment Facilitates Better Use of Data

Let’s say a big data company’s analytics reveal more success with one program over another. In a DevOps automated environment, programmers can use this data to produce better products.

Operational workers can then use this data for their next move, which can in turn present opportunities for more coding innovation, showing how both can facilitate productivity in the other.

These are just some of the advantages of automating workforces in a DevOps culture, and the benefits it can provide to product development.