Market Moves Toward Unified SQL and RAG Tooling
What happened
The enterprise search market is seeing a push for unified tooling that combines traditional SQL with RAG systems for unstructured data. This trend is exemplified by companies like Mode, which recently released a new SQL editor designed to serve both data analysts and agentic workflows, blurring the line between structured and unstructured data analysis.
Why it matters
- Mode's parent company, ThoughtSpot, acquired it in a $200 million cash and equity transaction to combine Mode's code-first approach with ThoughtSpot's natural language, self-service analytics. This deal is expected to increase ThoughtSpot's annual recurring revenue to over $150 million. - The unification of SQL and RAG is driven by the need to analyze the 80-90% of enterprise data that is unstructured and growing faster than structured data. Traditional SQL is insufficient for formats like text, images, and audio, which require techniques like natural language processing. - A key technical challenge in this unification is creating a common master data model that can apply consistent business terminology across both structured tables and unstructured content. Without this, it's difficult to accurately join insights from both data types. - Competitors are also addressing this trend. Glean, for instance, uses a knowledge graph to map relationships between content, people, and activities, combining vector and lexical search in its RAG systems to enhance relevance. - The evolution of RAG is moving towards "Agentic RAG," where AI agents can autonomously decide whether to query a SQL database for structured data or a vector database for unstructured information. This allows the system to handle more complex, multi-step queries that require reasoning. - This agent-based approach often involves a multi-tool architecture. For example, one agent might transform a user's natural language question into a SQL query, another validates it, and a third retrieves unstructured context from a vector database before synthesizing a final answer.
Key numbers
- - Mode's parent company, ThoughtSpot, acquired it in a $200 million cash and equity transaction to combine Mode's code-first approach with ThoughtSpot's natural language, self-service analytics.
- This deal is expected to increase ThoughtSpot's annual recurring revenue to over $150 million.
- The unification of SQL and RAG is driven by the need to analyze the 80-90% of enterprise data that is unstructured and growing faster than structured data.
What happens next
- This deal is expected to increase ThoughtSpot's annual recurring revenue to over $150 million.
Quick answers
What happened in Market Moves Toward Unified SQL and RAG Tooling?
The enterprise search market is seeing a push for unified tooling that combines traditional SQL with RAG systems for unstructured data. This trend is exemplified by companies like Mode, which recently released a new SQL editor designed to serve both data analysts and agentic workflows, blurring the line between structured and unstructured data analysis.
Why does Market Moves Toward Unified SQL and RAG Tooling matter?
Mode's parent company, ThoughtSpot, acquired it in a $200 million cash and equity transaction to combine Mode's code-first approach with ThoughtSpot's natural language, self-service analytics. This deal is expected to increase ThoughtSpot's annual recurring revenue to over $150 million. The unification of SQL and RAG is driven by the need to analyze the 80-90% of enterprise data that is unstructured and growing faster than structured data. Traditional SQL is insufficient for formats like text, images, and audio, which require techniques like natural language processing. A key technical challenge in this unification is creating a common master data model that can apply consistent business terminology across both structured tables and unstructured content. Without this, it's difficult to accurately join insights from both data types. Competitors are also addressing this trend. Glean, for instance, uses a knowledge graph to map relationships between content, people, and activities, combining vector and lexical search in its RAG systems to enhance relevance. The evolution of RAG is moving towards "Agentic RAG," where AI agents can autonomously decide whether to query a SQL database for structured data or a vector database for unstructured information. This allows the system to handle more complex, multi-step queries that require reasoning. This agent-based approach often involves a multi-tool architecture. For example, one agent might transform a user's natural language question into a SQL query, another validates it, and a third retrieves unstructured context from a vector database before synthesizing a final answer.
