A broad understanding is that a data warehouse is a fully schematized data storage and processing platform whereas a data lake is more fluid in its working as the name suggests. Given below are the few steps which are done differently in a data warehouse versus a data lake:
In a data warehouse, first, you define metadata, and then you add data to it, but in a data lake, first, you ingest data and then define the metadata around it. In this way, you can assign multiple metadata tags to the same data set.
A data warehouse can scale up to few terra bytes whereas in a data lake you can store up to few petabytes of data.
In a data lake, we can store data and process it separately. To know more about how this is made possible, read about various technology stacks used in a data lake. Some use cases may require more storage whereas others need more processing power. Accordingly, we can scale any of these two. It can save a lot of money for the company.
A data warehouse contains small datasets; hence its data processing speed is good. But a data lake holds large datasets which takes a toll on its processing speed.
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