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Four Common Data Engineering Pitfalls (and How to Avoid Them)


Your company has made it a strategic priority to become more data-driven. Good! A major anticipated component of this transition is to implement new data technology (e.g., a data lake). Resources are thrown at identifying source systems and pulling information into a new, analytically-focused data repository or an even bigger data Lake. Time is spent creating an ETL pipeline to move data from one place to another. Web endpoints are created to facilitate access for data customers. Dashboards are created that show information available in this centralized and optimized data source. At a brief with the company executive team 12 months later, the excited response from the C-level is a resounding: So how has any of this effort made us more data-driven?



This post first appeared on Elder Research Data Science & Machine Learning Blog, please read the originial post: here

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Four Common Data Engineering Pitfalls (and How to Avoid Them)

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