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.

Vanna AISince 2023-08

Vanna AI is an open-source Python-based RAG (Retrieval-Augmented Generation) framework engineered to generate high-accuracy SQL queries from plain English questions. Unlike general-purpose chatbots that frequently hallucinate non-existent database columns and tables, Vanna trains specifically on your database schema, table DDL, documentation, and historical query logs. When a business user or developer asks a natural language question, Vanna retrieves relevant schema definitions and validated SQL examples to construct an accurate, executable SQL query for PostgreSQL, Snowflake, BigQuery, MySQL, SQLite, or SQL Server. With over 12,000 GitHub stars, Vanna allows organizations to deploy self-hosted text-to-SQL agents inside Slack, Streamlit dashboards, or internal REST APIs without exposing private database records to third parties.

Vanna operates on a simple two-phase architecture: training and inference. In the training phase, you feed Vanna your database DDL statements, business documentation, and validated SQL query pairs, which Vanna stores in a vector database (such as ChromaDB, Qdrant, or Pinecone). During inference, Vanna uses semantic vector search to find the closest matching schema definitions and verified SQL patterns, injecting them into the LLM context (supporting OpenAI, Anthropic, Mistral, and local Ollama models) to synthesize the target SQL query. Vanna can automatically execute the query against your database connection, generate interactive Plotly charts, and explain the SQL logic in plain English.