The Top 5 Data Preparation Challenges to Get Big Data Pipelines to Run in Production

RAMESH MENON | March 30, 2019

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Many organizations have been looking to big data to drive game-changing business insights and operational agility, but big data has turned out to be so complex and costly to configure, deploy and manage, most data projects never make it into production. The agility businesses require today means being able to constantly add new analytics use cases, but so much of the resources available are consumed by the sheer ongoing maintenance of the pipelines that were already built. So what can organizations do to overcome the problem? After data is ingested into a data lake, data engineers need to transform this data in preparation for downstream use by business analysts and data scientists. Challenges in data preparation tend to be a collection of issues that add up over time to create ongoing maintenance and management issues.

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