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Why Big Data Strategies Need DevOps

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Applying DevOps concepts can have great benefits to any big data initiatives, but the analytics teams still choose not to use these methodologies. Applications based on the components of big data ecosystem need to be hardened in order to run in production, and DevOps can be included as an important part of that.

What is DevOps?

The idea behind DevOps is to tear down the barriers that stand between IT infrastructure administrators and software developers, in order to make sure that everyone’s focused on a singular goal. This requires a bit of cross-training from both sides so the used terminology is understood by everyone. After the completion of training, clear lines of direction and communication can be established, with a clear aim of continuous improvement. Both ends will be able to bring software features and fixes to end users faster, as DevOps enables them to work in tandem to tune production infrastructure components and test environments to meet new software requirements.

Big data analysts know how tough and complex it is to extract meaningful and accurate answers from big data. Big data software developers lack coordination in many enterprises, which often makes things more challenging and big data projects remain siloed for different …

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