Data lakehouse solutions

Summary

• A data lakehouse combines the open, low-cost storage of a data lake with the reliability and query performance of a data warehouse, so one governed copy of data serves BI, analytics, AI, and streaming.
• The core is a transactional storage layer: open table formats like Delta Lake and Apache Iceberg add ACID transactions to files in cloud object storage.
• Strong lakehouse solutions are open (no lock-in), transactional, governed in one place, and able to scale storage and compute independently.
• On Databricks, the Data Intelligence Platform delivers the lakehouse, with Databricks SQL providing warehouse-grade performance for BI on the same data used for AI.
• Because every workload reads one governed copy of data, a lakehouse removes the cost and drift of keeping parallel lake and warehouse systems in sync.

Data lakehouse solutions

A data lakehouse solution combines the open, low-cost storage of a data lake with the reliability and query performance of a data warehouse on one system, so BI, analytics, AI, and streaming run on a single copy of governed data instead of separate stacks. The strongest solutions share a few traits: data sits in open formats like Delta Lake and Apache Iceberg on your own cloud storage, writes are transactional (ACID), governance is defined once across every workload, and storage and compute scale independently. On Databricks, the Data Intelligence Platform delivers the lakehouse, with Databricks SQL providing warehouse-grade performance for BI on the same data that feeds AI.

What is a data lakehouse?

A data lakehouse is an open, unified system that adds the data management and performance of a warehouse directly to low-cost object storage. Its core is a transactional storage layer: open table formats such as Delta Lake and Apache Iceberg add ACID transactions on top of files in cloud storage, so every write either fully succeeds or rolls back, which prevents the corruption that concurrent writes can cause in a plain data lake. Because one governed copy of data serves every workload, a lakehouse removes the usual need to keep parallel copies in both a lake and a warehouse, and it handles structured, semi-structured, and unstructured data in the same place.

Key capabilities to evaluate

Capability	Why it matters
Open storage formats	Data in open formats (Parquet, Delta Lake, Iceberg) stays accessible to many engines and avoids lock-in.
ACID transactions	Reliable reads and writes when many users and jobs touch the same tables concurrently.
Storage and compute separation	Scale each independently to support more users and larger data without overprovisioning.
Fine-grained governance and lineage	One access-control, audit, and lineage model across tables, files, models, and dashboards.
Declarative, reliable pipelines	Built-in data-quality checks and recovery so pipelines stay dependable in production.
Diverse workloads on one copy	SQL analytics, machine learning, and streaming run on the same governed data.
Direct BI access	BI tools query source data directly, reducing staleness and extra copies.
Multicloud consistency	One management, security, and governance model across clouds.

How Databricks delivers a data lakehouse

The Databricks Data Intelligence Platform puts every workload on one governed copy of data in your own cloud account across AWS, Azure, and Google Cloud.

• Open storage. Delta Lake stores data as Parquet files with a transaction log that records every change and supports Time Travel to query historical snapshots. Apache Iceberg tables are supported too, so data stays open.
• Separation of storage and compute. Compute scales independently of storage, and data stays in your cloud account with cloud-native networking and access controls honored.
• Governance with Unity Catalog. Unity Catalog provides one fine-grained security model, with access control, auditing, discovery, and column-level lineage across data and AI assets.
• Warehousing with Databricks SQL. Databricks SQL and serverless SQL warehouses deliver warehouse-grade performance for BI and analytics directly on lakehouse tables.
• Pipelines and orchestration. Lakeflow Pipelines offer declarative ETL with built-in data testing, and Lakeflow Jobs orchestrate work across SQL, notebooks, and other tasks.
• AI, BI, and streaming. AI/BI Genie answers questions in natural language, AI/BI Dashboards handle reporting, Structured Streaming powers real-time data, and machine learning runs on the same governed tables.

For background, see open lakehouse and open data standards and data lakehouse examples.

Use cases

• Unified BI and AI. Run dashboards and machine learning on one governed copy of data instead of syncing a separate warehouse and lake.
• Warehouse modernization. Move warehouse workloads onto open lakehouse tables so analytics and AI share the same source.
• Real-time analytics. Combine streaming ingestion with SQL and AI on the same tables for fresh insight.

FAQs

What's the difference between a data lakehouse and a data warehouse?

A warehouse is tuned for structured SQL analytics, often on a proprietary format. A lakehouse adds warehouse-grade reliability and performance to open storage, so it serves structured and unstructured data and supports BI, machine learning, and streaming on one copy.

Do I have to move my data into a proprietary format?

No. A lakehouse keeps data in open formats such as Delta Lake and Apache Iceberg in your own cloud storage, so many engines and tools can read it and you avoid lock-in.

Can BI tools query the lakehouse directly?

Yes. With Databricks SQL and serverless SQL warehouses, BI tools query lakehouse tables directly, which reduces stale copies and keeps reporting close to source data.

Does one platform really serve both BI and AI?

Yes. Because analytics and AI read the same governed tables through Unity Catalog, teams avoid duplicating data across systems and keep one consistent set of controls.