Resources
Browse resources tailored to your team, and learn how the best data and AI teams are using DataHub.
New Research: The state of context management for agentic AI
The findings reveal a market at an inflection point: high confidence, real infrastructure gaps, and a correction already underway.
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What Is a Context Catalog? Why Data Catalogs Aren’t Enough for the AI Era
A context catalog makes metadata usable by AI agents and humans. Learn…
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Context-Aware AI Agents: Why Most Aren’t (and What It Takes to Build One That Is)
Context-aware AI agents need more than clever prompts. See why context-awareness is…
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The Glossary Is The Start: Building the Context Layer That Makes AI Work in Financial Services
Why the context layer in financial services starts at the glossary, not…
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The Context Layer for AI: What Enterprises Get Wrong
Everyone’s building a context layer for AI. Most are building the wrong…
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Context Layer vs Semantic Layer
Context layer vs semantic layer: What each does, how they relate, and…
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RAG vs Context Management
RAG is a retrieval pattern. Context management is the infrastructure that makes…
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How to Use the DataHub Cloud Value Estimator
Use this business value estimator to build a credible business case, grounded…
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Launching our Connector to GCP Knowledge Catalog
DataHub’s GCP Knowledge Catalog connector supports bidirectional sync across Vertex AI, BigQuery,…
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News
DataHub and Google Deepen Collaboration in Unifying Multi-Platform Context and Accelerating Trusted AI Deployments
DataHub commissioned independent research firm TrendCandy to survey 250 IT and data…
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DataHub Now Integrates with Google BigLake Iceberg REST Catalog
DataHub now ingests Iceberg metadata from Google BigLake’s REST Catalog. No duplicate…
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What Is a Context Engineer (and Is It Your Next Role)?
Context engineers build the systems that make AI agents reliable. Here’s what…
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Context Engineering vs Prompt Engineering
Context engineering vs prompt engineering: What changed, what’s different, and the infrastructure…
