What is Data Lakehouse?
A data architecture that combines the low-cost storage of data lakes with the structured querying and performance of data warehouses.
Definition
A data lakehouse merges two previously separate architectures: data lakes (cheap storage for raw, unstructured data) and data warehouses (structured, optimized storage for analytics queries). The lakehouse stores data in open formats (Parquet, Delta Lake, Iceberg) on object storage (S3, GCS, ADLS) while providing the ACID transactions, schema enforcement, and query performance traditionally associated with data warehouses. Databricks popularized the term and architecture, but Snowflake, BigQuery, and others have adopted similar concepts.
Why It Matters
Before lakehouses, companies maintained separate data lakes (for raw data and ML workloads) and data warehouses (for BI and analytics). This created data duplication, sync complexity, and higher costs. The lakehouse eliminates the need for two systems by providing warehouse-quality performance on lake-stored data. For B2B data operations, this means your enrichment data, intent signals, and CRM exports can live in one system that serves both analytics and machine learning workloads.
Example
A B2B company stores raw job posting data, website visitor logs, and CRM exports in a Delta Lake on S3. The data team runs SQL queries against this data using Databricks SQL for pipeline analytics and dashboards. The data science team trains intent prediction models on the same data using Spark. One storage layer serves both use cases without moving data between systems.