When working with clients as a freelance Data Engineer, I am often asked what the difference between Data Lakes vs. Warehouses is or asked which of the two is better as a whole.
While both are great solutions for specific problem sets, they still have their differences.
A Data Lake is a capture of every aspect of your business operation. When using a Data Lake, your data is stored in its raw format (aka natural form), usually as object blobs or files. The primary function of a Data Lake is to store, process, and secure large amounts of data. This data can be structured, unstructured, or even semi-structured.
Benefits of Data Lakes:
- Store data in its native form
- Support various data types and all of the users
- Easily adaptable
A Data Warehouse is a central repository of data (or information, in other words) that can be used as a tool to make better decisions for your business. Data Warehouses are more structured than Data Lakes, so they often contain multiple databases. These databases house data organized into tables and columns, which enables in-depth analysis, machine learning, and further data engineering to occur.
Benefits of Data Warehouses:
- Data is often loaded only after the use case is defined
- Data can be processed, organized, and/or transformed
- Insights from the data can be provided faster
- Current and historical data is easily recalled for reporting
- Maintains a consistent schema shared across applications
Think of a Data Lake as a deep pool of raw data that doesn't yet have a specified purpose. A Data Warehouse is more of a structured form of housing data. This data is already filtered and has been processed for a specific purpose.
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