Data lakes vs. data warehouses

Data lakes and warehouses are essential tools for storing large amounts of enterprise data, which is crucial for analytics and decision-making. A data warehouse is a central repository of structured data that follows the ETL process and uses schema-on-write model. It's best suited for analytics and has built-in features or direct connection with business intelligence tools. On the other hand, a data lake stores structured and unstructured data using ELT method and schema-on-read approach. It is used for big data analytics, machine learning, predictive analytics, etc. Data lakes are not easily accessible or joinable using SQL or most BI platforms. Both technologies complement each other in the modern data stack, with a data warehouse being the primary repository for structured business data and a data lake serving as a central repository for both structured and unstructured data.

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