What is a Data Lake?
A data lake is a reservoir that can store vast amounts of raw data in its native format. This data can be
- Structured data from relational databases (rows and columns),
- Structured data from NoSQL databases (like MongoDB, Cassandra, etc.),
- Semi-structured data (CSV, logs, XML, JSON),
- Unstructured data (emails, documents, PDFs) and
- Binary data (images, audio, video).
The purpose of a data lake, a capacious and agile platform is to hold all the data of an enterprise at a central platform. By this, we can do comprehensive reporting, visualization, analytics, and eventually glean deep business insights.
How is the working of a data lake different from a data warehouse?
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:
Decoupling of metadata and data
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.
Decoupling of storage and processing
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.