Loading...
Databricks's public acquisition history (7 notable deals) mapped against the engineering-signal panel we publish.
Databricks (HQ San Francisco, CA) is one of the public-company acquirers whose M&A cadence shapes the technical-startup exit landscape. This page summarizes their publicly disclosed acquisitions, their stated focus areas, and how those map against the engineering-acceleration signals VC Deal Flow Signal tracks. Databricks M&A is the most AI-aggressive of any infrastructure acquirer. MosaicML (2023, $1.3B) gave them open-source LLM training stack; Tabular (2024) brought them the Iceberg open-table format; Lilac (2024) added data curation. They scout open-source AI primitives and the lakehouse-adjacent governance layer. No private data is published here — every deal listed below was announced via press release, SEC filing, or both.
7
Notable deals
4
Focus sectors
12
Companies we track
Databricks M&A is the most AI-aggressive of any infrastructure acquirer. MosaicML (2023, $1.3B) gave them open-source LLM training stack; Tabular (2024) brought them the Iceberg open-table format; Lilac (2024) added data curation. They scout open-source AI primitives and the lakehouse-adjacent governance layer.
Databricks scouts open-source AI/ML training and inference primitives, lakehouse governance, and applied AI. Engineering-signal hallmarks: heavy PyTorch / JAX usage, open-source maintainer activity (Spark, Delta, Iceberg, MLflow ecosystems), strong distributed-training infrastructure experience.
Sorted by year (most recent first). Every deal here was announced publicly via press release, SEC filing, or both.
Apache Iceberg lakehouse table format.
Data curation and analysis.
AI-native analytics interface.
Streaming postgres / serverless OLTP.
Open-source LLM training and inference.
Data governance and access control.
Low-code analytics (Bamboolib).
Frontier labs, model providers, open-weight checkpoints, and the applied-AI layer on top. A single page mapping who builds, who funds, and who leads in ai & machine learning.
Compute, orchestration, inference, and the serving layer underneath the model providers. A single page mapping who builds, who funds, and who leads in ai infrastructure.
OLTP, OLAP, vector stores, embedded engines, and the storage layer underneath every modern app. A single page mapping who builds, who funds, and who leads in databases.
Warehousing, transformation, BI, and the analyst-facing query surface on top of operational data. A single page mapping who builds, who funds, and who leads in data analytics.
We do not claim these companies are acquisition targets. They are simply companies in the engineering-signal panel that sit in the same sectors Databricks has historically acquired in.
This page documents 7 notable public acquisitions by Databricks — every deal here was announced via press release, SEC filing, or both. Databricks's full acquisition history may include smaller, undisclosed talent acquisitions; we list only the publicly documented deals that materially shaped their direction.
Databricks scouts open-source AI/ML training and inference primitives, lakehouse governance, and applied AI. Engineering-signal hallmarks: heavy PyTorch / JAX usage, open-source maintainer activity (Spark, Delta, Iceberg, MLflow ecosystems), strong distributed-training infrastructure experience.
Databricks M&A is the most AI-aggressive of any infrastructure acquirer. MosaicML (2023, $1.3B) gave them open-source LLM training stack; Tabular (2024) brought them the Iceberg open-table format; Lilac (2024) added data curation. They scout open-source AI primitives and the lakehouse-adjacent governance layer.
No. This page is an independent summary of Databricks's publicly disclosed acquisitions and stated focus areas. Databricks has not endorsed, paid for, or reviewed this page. All deals listed are sourced from their own press releases, SEC filings, or both. We do not publish private deals or speculation about future acquisitions.
Two workflows. (1) Pattern matching: when scouting acquisition targets, the 7-deal history above is a published reference for what Databricks actually buys — useful for triangulating "would they buy this?" judgments. (2) Sector overlap: the focus-sectors mapping connects Databricks's historical M&A pattern to the engineering-signal panel we publish, so analysts can correlate acquisition pace with sector-level signal acceleration.
Weekly digest of ai-ml, ai-infra, database momentum, surfaced 3 to 6 weeks before announcements.
See First Look