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Data lake, data warehouse, and database…what's the difference?

There are so many buzzwords these days regarding data management, such as data lakes, data warehouses, databases, data marts, and data swamps. In a recent Oracle Big Data blog post, Sherry Tiao walks through them and covers the definitions, the key differences, and what we see for the future.

The following excerpts from the blog post will give you an idea of what they are and how they relate to each other.

  • Structured data: Let’s say you have a rewards card with a grocery chain. The database might hold your most recent purchases, with a goal to analyze current shopper trends. The data warehouse might hold a record of all of the items you’ve ever bought and it would be optimized so that data scientists could more easily analyze all of that structured data.
  • Unstructured data: Any data that is unstructured but still valuable—such as the social sentiment that the rewards program collects or advertising results—can be stored in a data lake and works with both the data warehouse and database. A data lake that is messy and unmanageable becomes a data swamp. New technologies such as a data catalog will steadily make it simpler to find and use the data in a data lake.
  • Limited-scope data: A data mart is used by individual departments or groups and is intentionally limited in scope because it looks at what users need right now versus all the data that already exists.

While we expect more companies will move their unstructured data to data lakes on the cloud, where it’s more cost-effective to store and easier to move when it’s needed, we also expect they will continue to keep their structured data in the database or data warehouse. This workload that involves the database, data warehouse, and data lake in different ways is one that works well. We predict we’ll continue to see more of this for the foreseeable future.

Read the entire blog for full definitions of the terms highlighted above—plus a few more—and an illuminating discussion about the key differences.

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