17 February, 2017
Your Big Data Strategy Needs DevOps
Many big data analytics teams choose to not use DevOps methodologies, but there are real benefits to applying DevOps concepts to those big data initiatives.
Extracting accurate and meaningful answers from big data is tough. It’s often made more challenging given the way big data software developers and IT operations lack coordination in many enterprises. Even though an IT organization may practice sound DevOps strategies for other supported applications, big data projects often remain siloed for a variety of reasons.
Today we’re going to look at what DevOps is and why many big data project teams choose to not use DevOps methodologies. We’ll then move on to the benefits that DevOps can provide, as well as any challenges that might be faced along the way when moving big data to a DevOps process model.
But before we do that, let’s first take a step back to define what DevOps is, and learn why it’s become so popular. The idea of DevOps is to tear down the silos between software developers and IT infrastructure administrators to make sure everyone is focused on a singular goal. A bit of cross-training is required on both sides of the house to the point where processes and terminology used are understood by all. Then once training is complete, clear lines of communication and direction can be established with an aim of continuous improvement. Both teams work in tandem to test environments, tune production infrastructure components to meet new software requirements — and ultimately — bring software fixes and features to end users more rapidly.