Why Can't We Just Store All This Information Into a Data Warehouse?
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Many organizations have implemented data lakes to great success, giving them a tactical business edge through the use of data analysis and predictive analytics.

This course covers the basics of data lakes, how they are different from data warehouses, and the components that make up a successful data lake.

Learning Objectives

  • Understand the difference between data warehouses and data lakes
  • Know what qualities make up a good data lake.
  • Learn about AWS Lake Formation and how it can transform the process of creating a data lake from taking months to days

Intended Audience

This course is intended for anyone who is responsible for managing business data or for those interested in creating a data lake in general.


To get the most out of this course, you should have a decent understanding of cloud computing and cloud architectures, specifically with Amazon Web Services.


Why can't we just store all this information into a data warehouse?

Well, This was exactly what was happening for a long period of time. Unfortunately, as the speed of business has increased, and so has the sheer volume of data. Data warehouses were unable to keep up with the amount of curating and scaling required to support such volumes of data. It was becoming cost-prohibitive and slowing down query speed to try and maintain all of this data in an active database.

So the advent of the data lake became a necessity. We needed a place where we could store large volumes of information for cheap.


About the Author

William Meadows is a passionately curious human currently living in the Bay Area in California. His career has included working with lasers, teaching teenagers how to code, and creating classes about cloud technology that are taught all over the world. His dedication to completing goals and helping others is what brings meaning to his life. In his free time, he enjoys reading Reddit, playing video games, and writing books.