Handling Real-Time Data Operations in the Enterprise

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Getting DataOps right is crucial to your late-stage big data projects.

Companies need to understand there is a different level of operational requirements when you’re exposing a data pipeline. A data pipeline needs love and attention. For big data, this isn’t just making sure cluster processes are running. A DataOps team needs to do that and keep an eye on the data.

With big data, we’re often dealing with unstructured data or data coming from unreliable sources. This means someone needs to be in charge of validating the data in some fashion. This is where organizations get into the garbage-in-garbage-out downward cycle that leads to failures. If this dirty data proliferates and propagates to other systems, we open Pandora’s box of unintended consequences. The DataOps team needs to watch out for data issues and fix them before they get copied around.

These data quality issues bring a new level of potential problems for real-time systems.

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