Data Hubs, Data Lakes and Data Warehouses

How They Are Different and Why They Are Better Together

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Many data and analytics leaders think of data hubs, data lakes and data warehouses as interchangeable alternatives. In reality, each of these architectural patterns has a different primary purpose. When they are combined, they can support increasingly complex, diverse and distributed workloads.

“Data warehouses and data lakes are structures supporting analytic workloads. Data hubs are different — their main focus is enabling data sharing and governance.”

– Ted Friedman and Nick Heudecker, Gartner

Read the full report to understand:

  • The key differences between data lakes, data hubs, and data warehouses.
  • The options that provide the best support for specific business requirements.
  • How these three structures can be combined to support many use cases.

Gartner, Data Hubs, Data Lakes and Data Warehouses: How They Are Different and Why They Are Better Together, 13 February 2020, Ted Friedman, Nick Heudecker.
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally, and is used herein with permission. All rights reserved.

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