Databricks Data Intelligence Platform

What is Databricks Data Intelligence Platform?

The Databricks Data Intelligence Platform is a managed service for working with data and AI in one environment. It is built around the lakehouse idea: an organization keeps a single copy of its data in its own cloud object storage, in open table formats such as Delta Lake and Apache Iceberg, and runs data engineering, SQL analytics, machine learning and AI workloads directly on it rather than copying data into separate warehouse and data science systems. The platform runs on AWS, Microsoft Azure and Google Cloud, and much of its processing is built on Apache Spark.

Unity Catalog sits at the center, recording tables, files, models, dashboards and AI agents and applying one set of permissions, lineage tracking and auditing to all of them. Around it are Lakeflow for ingesting and transforming data, serverless SQL warehouses sold under the Lakehouse name for business intelligence, notebooks and managed MLflow for machine learning, Lakebase for operational Postgres databases, Databricks Apps for hosting internal applications, and tools for sharing data with other organizations.

Since 2023 Databricks has concentrated on AI. Genie lets business users ask questions about data in plain language, and Genie One, introduced in June 2026, extends it into an AI coworker that drafts reports and automates routine tasks. Agent Bricks helps teams build, evaluate and govern AI agents using models from providers including OpenAI, Anthropic and Google, while Unity Gateway controls which models and tools agents can use and what they cost. Databricks introduced the Data Intelligence Platform name in November 2023 and by mid-2026 was presenting the same product as the Databricks Data + AI Platform. It competes with the Snowflake AI Data Cloud and with analytics services from the major cloud providers.

Key features of Databricks Data Intelligence Platform

1. 01

   Lakehouse storage on open formats

   Data stays in the customer’s cloud object storage as Delta Lake or Apache Iceberg tables, which support ACID transactions, schema enforcement and time travel while remaining readable by other engines.

2. 02

   Unity Catalog governance

   A single catalog manages permissions, lineage, auditing and discovery for tables, files, machine learning models, dashboards and AI agents across workspaces and clouds.

3. 03

   Lakeflow data engineering

   Lakeflow provides connectors that ingest data from databases and business applications, declarative pipelines for batch and streaming transformations, and a job orchestrator for scheduling production workflows.

4. 04

   Serverless SQL warehouses

   SQL warehouses, sold under the Lakehouse name, run business intelligence queries and dashboards on lakehouse data and connect to reporting tools such as Tableau, Power BI and Looker.

5. 05

   Genie AI assistant

   Genie answers business questions in natural language using an organization’s governed data and business definitions, and Genie One extends it into an agent that produces reports and automates routine work.

6. 06

   Agent Bricks and model serving

   Teams build, evaluate and deploy AI agents and machine learning models with a choice of commercial and open models, tracked through MLflow and served from managed endpoints.

7. 07

   Lakebase operational database

   Lakebase is a managed, autoscaling Postgres database for applications and AI agents that keeps transactional data close to the lakehouse, where it can be analyzed without separate export jobs.

8. 08

   Notebooks and Databricks Apps

   Collaborative notebooks support Python, SQL, Scala and R, and Databricks Apps hosts internal data and AI applications on serverless compute with centrally managed permissions.

Who uses Databricks Data Intelligence Platform?

• Banks, retailers and telecommunications companies consolidate data lakes and warehouses on one platform for reporting, fraud detection and customer analytics.
• Data engineering teams build batch and streaming pipelines that ingest data from applications and devices and prepare it for analytics and machine learning.
• Data science teams train, track and deploy machine learning models, from demand forecasts to recommendation systems, using notebooks, MLflow and model serving.
• Enterprises build AI agents and chat assistants grounded in internal documents and governed tables, with access rules and cost limits applied centrally.
• Business analysts build SQL dashboards or ask Genie questions in plain language instead of waiting for custom reports from data teams.

History of Databricks Data Intelligence Platform

Databricks launched its first product, Databricks Cloud, in June 2014, a year after the company was founded. It gave customers a hosted environment for running Apache Spark jobs and notebooks without setting up clusters themselves. The product was later sold as the Unified Analytics Platform, Microsoft brought it to Azure as Azure Databricks in 2018, and support for Google Cloud followed in 2021.

As companies stored more data in cloud object storage, Databricks released Delta Lake in 2019 to add reliable transactions to data lakes, introduced SQL Analytics, later Databricks SQL, in 2020, and began calling the combination the Lakehouse Platform. After buying MosaicML in 2023, it repositioned the product as the Data Intelligence Platform that November to emphasize AI built into each layer. Unity Catalog was open-sourced in 2024, Agent Bricks and Lakebase arrived in 2025, and in 2026 Databricks added Genie Code, Genie One and the Lakewatch security product while adopting the Data + AI Platform name.

1. 2014

   Databricks launches Databricks Cloud, a hosted platform for running Apache Spark.

2. 2018

   Azure Databricks becomes generally available as a first-party Microsoft Azure service.

3. 2019

   Databricks releases Delta Lake as an open-source storage layer for data lakes.

4. 2020

   Databricks introduces SQL Analytics, later renamed Databricks SQL.

5. 2023

   Databricks repositions its platform as the Data Intelligence Platform after acquiring MosaicML.

6. 2024

   Databricks open-sources Unity Catalog and acquires Tabular, founded by the creators of Apache Iceberg.

7. 2025

   Databricks launches Agent Bricks for building AI agents and Lakebase, a managed Postgres database.

8. 2026

   Genie One and Lakewatch launch, and Databricks markets the product as the Databricks Data + AI Platform.

Databricks Data Intelligence Platform pricing

Databricks Data Intelligence Platform alternatives

Profiled on SoftwareLore:

Also considerGoogle BigQuery, Microsoft Fabric, Amazon SageMaker and Cloudera Data Platform

Databricks Data Intelligence Platform: frequently asked questions

Sources

1. The Databricks Data + AI Platform databricks.com
2. Databricks pricing databricks.com
3. Databricks puts AI at core of new Data Intelligence Platform, TechTarget techtarget.com
4. Databricks launches Genie One, press release databricks.com
5. Databricks unveils Spark-based cloud platform, 2014 databricks.com

Last reviewed September 17, 2026. Spotted an error? Send a correction.