RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece
RAG is only as good as the metadata behind it. Large language models are only as useful as the context you give them. "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece" keeps the conversation practical as Varun Joshi works through where schemas, lineage, query history, and dbt models fit in data engineering workflows.
Session details
Large language models are only as useful as the context you give them. For data engineers, that context is your schema, your lineage graph, your query history, your dbt models — and most teams haven't connected those dots yet. This session is a practical introduction to Retrieval-Augmented Generation from a data engineering lens. We'll cover what RAG actually does under the hood, why it matters more for data teams than most, and the four places it shows up naturally in the data engineering workflow: answering schema questions without digging through a stale catalog, grounding SQL agents in your actual table definitions, giving incident response agents access to historical pipeline context, and surfacing institutional knowledge that currently lives only in senior engineers' heads.
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Agenda facts
- Format
- Talk / Presentation
- Kind
- Conference Session
- Topic
- Artificial Intelligence and Machine Learning
- Level
- Intermediate
- Time
- Oct 29, 2026, 10:00 AM
- Room
- Room A
- Duration
- 60 min
Speakers
Link partners to speaker kits when available, or to the public speaker profile on mitechcon.net.
Varun Joshi
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LinkedIn
Session LinkedIn Post
RAG is only as good as the metadata behind it. Large language models are only as useful as the context you give them. "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece" keeps the conversation practical as Varun Joshi works through where schemas, lineage, query history, and dbt models fit in data engineering workflows. 📅 October 28–30, 2026 📍 Oakland University, Rochester, MI 🔗 https://www.mitechcon.net/sessions/rag-for-data-engineers-what-it-is-where-it-fits-and-why-your-metadata-is-the-missing-piece/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-rag-for-data-engineers-what-it-is-where-it-fits-and-why-your-metadata-is-the-missing-piece #MITechCon #RAG #DataEngineering #Metadata
Short social
Short Session Post
RAG is only as good as the metadata behind it. Learn where schemas, lineage, query history, and dbt models fit in data engineering workflows. https://www.mitechcon.net/sessions/rag-for-data-engineers-what-it-is-where-it-fits-and-why-your-metadata-is-the-missing-piece/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-rag-for-data-engineers-what-it-is-where-it-fits-and-why-your-metadata-is-the-missing-piece #MITechCon #RAG
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Newsletter Or Email Blurb
Feature this MITechCon 2026 session in your newsletter or email: "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece". RAG is only as good as the metadata behind it. Large language models are only as useful as the context you give them. "RAG for Data Engineers: What It Is, Where It Fits, and Why Your Metadata Is the Missing Piece" keeps the conversation practical as Varun Joshi works through where schemas, lineage, query history, and dbt models fit in data engineering workflows. Format: Conference Session. Topic: Artificial Intelligence and Machine Learning. Level: Intermediate. Scheduled for Oct 29, 2026, 10:00 AM in Room A. Learn more and share the session: https://www.mitechcon.net/sessions/rag-for-data-engineers-what-it-is-where-it-fits-and-why-your-metadata-is-the-missing-piece/?utm_source=partner-kit&utm_medium=partner&utm_campaign=mitechcon-2026&utm_content=session-rag-for-data-engineers-what-it-is-where-it-fits-and-why-your-metadata-is-the-missing-piece