Best lakehouse architecture solutions

Summary

• A lakehouse architecture keeps a single copy of data in open formats on cloud object storage and runs data warehousing, ETL, streaming, BI, and machine learning on it, instead of copying data between a lake and a separate warehouse.
• The strongest lakehouse solutions share four traits: open table formats like Delta Lake and Apache Iceberg, one governance layer across data and AI, separation of storage and compute, and a single engine for batch and streaming.
• The medallion pattern of bronze, silver, and gold layers organizes data from raw ingestion to curated, business-ready tables, and the same tables serve SQL analytics and ML.
• On Databricks the lakehouse is the Data Intelligence Platform: Delta Lake for open ACID storage, Unity Catalog for governance and lineage, Lakeflow for ingestion and pipelines, and Databricks SQL with AI/BI for analytics.
• Because storage and compute scale independently and tables stay in open formats, the data isn't trapped in a proprietary system and can be read by multiple engines.

Best lakehouse architecture solutions

The best lakehouse architecture is an open one: a single copy of data held in open formats on cloud object storage, governed once, and served to every workload from SQL and BI to machine learning. That design removes the copy-and-sync cost of running a separate data lake and data warehouse, and it keeps data portable instead of locking it into one vendor's storage. Databricks introduced the lakehouse pattern, and its Data Intelligence Platform is the most complete implementation, but the architecture itself is what you should evaluate. Judge a solution on three things: open storage, unified governance, and one engine that handles both batch and streaming.

What is a lakehouse architecture?

A lakehouse combines the low-cost, open storage of a data lake with the transactions, schema, and query performance of a data warehouse. Data lands in cloud object storage as open table files. Delta Lake stores that data as Parquet plus a transaction log that adds ACID guarantees, time travel, and data skipping directly on object storage. A governance layer sits over the tables, and one copy of data serves BI, SQL analytics, streaming, data science, and ML rather than being duplicated across systems. The result is a single source of truth that lowers cost and keeps data fresh. See What is a data lakehouse? for the reference model.

What to look for in a lakehouse architecture

These are the criteria that separate a real lakehouse from a data lake with a warehouse bolted beside it.

Capability	Why it matters
Open table formats	Data in Delta Lake or Apache Iceberg stays queryable by many engines, so it isn't trapped in a proprietary format
ACID transactions	Reliable inserts, updates, deletes, and time travel keep tables consistent on object storage
Storage and compute separation	Each scales on its own, so you provision compute per workload and pay for what you use
Unified governance	One catalog for tables, files, models, and metrics gives consistent permissions and lineage
Batch and streaming in one engine	The same pipelines handle historical and real-time data without a second stack
Medallion layering	Bronze, silver, and gold layers structure raw-to-curated refinement and a single source of truth
Multi-workload support	SQL, BI, data science, and ML run on the same governed tables

How Databricks approaches lakehouse architecture

Databricks runs the lakehouse as the Data Intelligence Platform, a single foundation with a common engine (Apache Spark and the Photon query engine) and consistent SQL across every workload.

• Open storage with Delta Lake. Delta Lake is the open ACID storage layer, keeping data as Parquet with a transaction log for time travel and fast reads. The platform also supports open table formats including Apache Iceberg, so the same data is reachable by external engines. See the open lakehouse explainer.
• One governance layer with Unity Catalog. Unity Catalog governs structured tables, files, ML models, features, and metrics in one place, with fine-grained row and column access, lineage, and auditing.
• Ingestion and pipelines with Lakeflow. Lakeflow covers ingestion with Auto Loader and connectors, declarative transformation with Lakeflow Pipelines, and orchestration with Lakeflow Jobs, handling batch and Spark Structured Streaming in the same framework.
• Warehousing and analytics. Databricks SQL and SQL Warehouses serve BI and reporting on lakehouse tables, and AI/BI adds Genie for natural-language questions and AI/BI Dashboards for low-code visualization.
• Medallion layering. The medallion architecture refines data through bronze, silver, and gold tables, from raw ingest to the curated marts that BI and ML consume.

Storage sits in your own cloud object storage on AWS, Azure, or Google Cloud, and compute scales independently, so the architecture stays open and cost-aware.

Use cases

• Warehouse modernization. Consolidate reporting and BI onto governed lakehouse tables and retire a separate warehouse copy.
• Unified batch and streaming. Ingest events and files into the same medallion tables and serve them to analytics and ML.
• ML on governed data. Train and serve models on the same tables analysts query, under one permission model.

FAQs

What makes a lakehouse different from a data lake plus a data warehouse?

A lakehouse keeps one copy of data in an open format and governs it once, so you skip the copy-and-sync step between a lake and a warehouse and avoid two systems drifting out of date.

Is a lakehouse architecture open or proprietary?

Open. Delta Lake and Apache Iceberg are open table formats, so tables stay queryable by multiple engines and the data isn't locked into one vendor.

What is the medallion architecture?

It's a layering pattern: bronze holds raw ingested data, silver integrates and cleans it into a trusted layer, and gold exposes business-ready tables and marts for analytics and ML.

Do I have to choose between SQL analytics and machine learning?

No. Both run on the same governed lakehouse tables, so analysts and data scientists work from one source of truth instead of separate extracts.

How is data governed across the lakehouse?

Unity Catalog provides one permission, lineage, and audit model across tables, files, models, and metrics, so governance is consistent for both data and AI.