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Data lake vs data warehouse: What’s the difference?

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Learn what a data lake is – an alternative to a data warehouse, designed to store structured, semi-structured and unstructured data at any scale.

Data lakes are optimized for big data processing and analytics, often using formats like Parquet or Iceberg with cloud object storage (S3, GCS, Azure Blob). They are ideal for data science, machine learning, and storing raw event data before transformation.

This video covers when to use a data lake vs a data warehouse, the rise of the lakehouse architecture which combines both, and how modern platforms blur the line between lakes and warehouses.

FAQs

A data lake is a storage system that holds structured, semi-structured and unstructured data at any scale, typically on cloud object storage. It is optimized for big data processing and analytics.

A data warehouse stores cleaned, structured data optimized for BI queries. A data lake stores raw data of any type at lower cost, but is less optimized for fast SQL analytics.

Use a data lake when you have large volumes of raw, unstructured, or semi-structured data, when you need to support data science and machine learning, or when storage cost is a primary concern.

A lakehouse combines features of data lakes and data warehouses – low-cost object storage for raw data, plus warehouse-style structure and query performance for analytics. Platforms like Databricks popularized this pattern.

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