Data Lake

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What Is a Data Lake?
A data lake is a low-cost storage environment, which typically houses petabytes of raw data. Unlike a data warehouse, a data lake can store both structured and unstructured data.

Also, data lake does not require a defined schema to store data, a characteristic known as “schema-on-read.” This flexibility in storage requirements is particularly useful for data scientists, data engineers, and developers, allowing them to access data for data discovery exercises and machine learning projects.

Data Lake vs. Data Warehouse
While both store data, each repository has its own requirements for storage, which makes it an ideal choice for different scenarios.

• For instance, data warehouses require a defined schema (i.e., in a structured data format) to fit specific data analytics requirements for data outputs, such as dashboards, data visualizations, and other BI (business intelligence) tasks.
• Data lakes, on the other hand, incorporate data from both relational and non-relational systems, allowing data scientists to incorporate structured and unstructured data into more data science projects.

Data warehouses tend to be more performant, but it comes at a higher cost. Data lakes may be slower in returning query results, but they have lower storage costs.