Unstructured x Databricks

Partnership

Databricks excels at structured data, but 80% of enterprise knowledge remains trapped in unstructured files—out of reach for your data teams and AI efforts.

Unstructured turns raw files into AI-ready data that plugs directly into your Databricks workflows.

Request Demo

Unlock 80% of Your Enterprise Data

Most enterprise knowledge is trapped in unstructured formats, like PDFs, docs, images, and more. Unstructured replaces brittle, manual pipelines with a scalable, Databricks-native solution—turning unstructured files into fuel for GenAI, search, and analytics.

Unstructured Platform	Unstructured Solution	Business Impact
Hard-to-access document data	Support for 60+ file types incl. PDFs, Office, images, and more	Boost usable data for RAG, agents, and analytics by 70–80%
Manual document processing bottlenecks	Scalable, automated ingestion and transformation	Reduce document processing time and workflow maintenance related efforts
Low quality data for LLMs and Vector Search	Smart chunking, enrichment, and embedding	Improve retrieval accuracy for GenAI
Complex GenAI implementation	End-to-end pipeline from raw documents to AI-ready data in Vector Search via Delta Tables	Cut GenAI project implementation time by automating data delivery

Bring Unstructured Data into Your Lakehouse

Databricks excels at structured data—Unstructured handles the rest. From PDFs to multimedia, we turn raw files into AI-ready formats so you can unify your data estate within Databricks.

Databricks Product	How It’s Enhanced with Unstructured
Volumes	Connect Unstructured directly to your Volumes to ingest unstructured files (PDFs, docs, images, audio, and more), extract clean structured content enriched with metadata, named entities and custom enrichments for downstream GenAI applications.
Delta Tables	Gather unstructured data from your entire organization, process it with Unstructured, then write processed outputs—like parsed text, structured tables, and RAG-ready chunks—directly into Delta Tables with schema alignment and metadata.
Unity Catalog	Maintain data lineage and access control by syncing processed outputs and metadata.
Vector Search	Generate embedding-ready, intelligently chunked content with rich metadata.
SQL Warehouse / Clusters	Make unstructured data queryable by converting it into structured, SQL-ready formats.

Key Features

Native File Access via Volumes
Mount and stream files from Databricks Volumes without third-party connectors. Supports 60+ formats with OCR, VLM, and parsing capabilities baked in.
Smarter Vector Search
Generate high-quality, semantically enriched inputs for embedding and store them for fast, accurate retrieval using Databricks Vector Search.
GenAI-Optimized Data Transformation
Produce retrieval-ready chunks enriched with metadata, captions, summaries, and entities—ideal for RAG and agentic workloads.
Full Metadata and Lineage Support
Track document provenance, apply access controls, and preserve semantic relationships between chunks with Unity Catalog compatibility.
Delta Table Integration
Write parsed and structured outputs directly to Delta Tables, auto-mapped to your schema with live sync for incremental updates.
Built for the Enterprise
Secure by design, with configuration-driven pipelines, robust orchestration, and governance-first architecture.

Use Cases

Relevant Blogs

Webinar: End-to-End RAG with Databricks

In this webinar we guide you through the end-to-end process of building a Retrieval Augmented Generation (RAG) application—from raw, unstructured data to a production-ready chatbot. In this session, you’ll learn how to turn your enterprise data into a powerful foundation for a context-aware AI assistant using Databricks and Unstructured.

E-Book

Download our free e-book: Databricks and Unstructured: Automate Enterprise Data to Fuel Your GenAI

E-Book