Databricks

DatabricksSince 2013-06

Databricks provides a unified platform for data engineering, data science, and machine learning. It enables teams to collaborate on big data processing and AI model development at scale.

Built on Apache Spark, Databricks offers a lakehouse architecture combining data warehousing and data lake capabilities. Founded in 2013 by the creators of Apache Spark.

UnstructuredSince 2023-01

Unstructured is the leading enterprise ETL (Extract, Transform, Load) platform engineered to prepare messy, unstructured business documents for Retrieval-Augmented Generation (RAG) and LLM fine-tuning. Over 80% of enterprise data lives in complex formats like scanned PDFs, PowerPoint decks, Word files, HTML tables, and email threads that break standard text scrapers. Unstructured utilizes specialized computer vision and vision-language models to segment documents into structural semantic elements (titles, paragraphs, headers, embedded tables, and image captions) while preserving exact spatial and hierarchical context. Available as an open-source Python library and a high-throughput serverless cloud API, Unstructured integrates directly with LangChain, LlamaIndex, and major vector databases to power mission-critical enterprise knowledge retrieval.

Unstructured processes more than 25 document formats through a multi-stage layout detection pipeline. Its vision models detect complex multi-column layouts, rotated text, complex mathematical notation, and embedded charts. For tabular data, Unstructured extracts full HTML and Markdown table structures, ensuring that financial balance sheets and technical specifications retain exact row-and-column relationships when embedded into vector stores. Unstructured includes automated semantic chunking strategies that respect document boundaries (chunk_by_title), preventing context fragmentation and maximizing retrieval accuracy in downstream RAG applications.