What is a Data Lakehouse? - Definition & Meaning

Definition

A data lakehouse is an architecture that combines the flexibility of a data lake (cheap storage for raw and structured data) with the structure and ACID transactions of a data warehouse. It provides one platform for BI, ML, and analytics.

Technical explanation

Traditionally: data lake for raw data and ML, data warehouse for structured reporting — two systems, data duplication, and sync issues. Lakehouse adds a transactional layer on object storage (S3, GCS) via open formats: Delta Lake (Databricks), Apache Iceberg, Apache Hudi. ACID transactions, schema enforcement, and time travel make it viable. One copy of data for BI, ML, and streaming. Open table formats reduce vendor lock-in. Delta Lake offers Z-ordering for faster queries; Iceberg has strong Apache integration.

How AVARC Solutions applies this

AVARC Solutions advises on lakehouse architecture and implements Delta Lake or Iceberg on existing object storage. For clients, we consolidate data lakes and warehouses into one lakehouse, delivering cost savings and faster ML iteration.

Practical examples

• A company migrating from separate data lake and Snowflake to a Delta Lake-based lakehouse on S3, cutting infrastructure costs by 40%.
• An ML team pulling features from the same lakehouse as the BI team, without ETL between systems.
• An analytics platform using Iceberg for schema evolution and time travel on historical data.

Related terms