Inside Analysis

Data Warehousing in the Age of Data Lakes

The emergence of big data analytics and the adoption of data lake architecture cause many to question the future of data warehousing. Yet recent surveys show that more than 60% of companies are still operating between 2 and 5 data warehouses. Many people have talked about eliminating the data warehouse altogether. But the reality is that the data warehouse offers value that the data lake doesn’t address and — vice versa. The real challenge is how to combine the data warehouse with a data lake architecture, modern data pipelines, and analytics use cases. Join us to learn how automation and agile data engineering step up to the challenges of data warehouse modernization.

You Will Learn:
-The key challenges of legacy data warehousing
-Architectural frameworks to position data warehousing as an integral component of a modern analytics ecosystem that works in tandem with a data lake
-The realities of modern data pipelines and the differences from the simple world of ETL
-Key concepts of agile data engineering and the role of automation in pipeline agility
-How data lake automation and data warehouse automation work together to build and operate a fast, scalable, and adaptable data management environment


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