Data lakes store raw, unstructured, semi-structured, and structured data. Lakehouses enforce schemas, allowing data standardization for advanced analytics.
Data lakes rely on external processing engines like Apache Spark, which may slow queries. Lakehouses integrate processing engines for efficient, real-time analytics.
Both data lakes and lakehouses are scalable and cost-effective, offering pay-as-you-go models. Lakehouses reduce costs further by minimizing data movement.
Traditional data lakes lack built-in concurrency and transactional support. Lakehouses support ACID properties, ensuring data consistency during concurrent operations.
Data lakes offer flexible governance relying on stewardship. Lakehouses prioritize schema enforcement, ensuring better control and compliance during data ingestion.
When choosing between a data lakehouse and a data warehouse, consider your organization's data structure, processing speed, scalability, and governance needs.