Collect data lakemetadata to better understand usage and start dataset refinement
Prioritize datasets for refinement and movement into a data warehouse
Use data preparation tools to take an iterative approach to data refinement
Data lakes can store vast amount of unrefined data for quick access and easy analytics experimentation. However, data lake efficacy rapidly declines when organizations need to consistently disseminate efficient, accurate, and curated data across the broader organization – which is the role of the enterprise data warehouse (EDW).
“In the Gartner Big Data Adoption Survey, 52% of respondents state that determining how to get value from big data is a top-three challenges, and EDW is the most likely way for them to derive value from big data.” It is now common practice to combine the relative strengths of the data lake and the EDW to better manage and derive big data insights.
In this complimentary Gartner Foundational Document, “Efficiently Evolving Data From the Data Lake to the Data Warehouse,” receive expert recommendations on how to:
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Learn How to Get the Most Out of Your Data
This complimentary dummies guide explains the cloud data warehouse and how it compares to other data platforms.
GARTNER FOUNDATIONAL DOCUMENT: EFFICIENTLY EVOLVING DATA FROM THE DATA LAKE TO THE DATA WAREHOUSE
Gartner, Efficiently Evolving Data From the Data Lake to the Data Warehouse, Rick Greenwald, Ehtisham Zaidi, 7 May 2019