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- Apple uses DataHub to manage machine learning metadata, custom entities, and AI governance across a fast-evolving data landscape.
- Visa replaced its custom catalog with DataHub, using API-powered metadata to scale governance, improve quality, and support AI workflows across global teams.
- Slack collapsed 6 years of metadata complexity into 3 days of progress with DataHub—unlocking extensible discovery, lineage, and governance across teams.
- Deutsche Telekom deployed DataHub to simplify discovery, resolve pipeline issues faster, and power AI platforms with metadata context.
- Chime uses DataHub Cloud to unify producers and consumers, enabling shared ownership, lineage visibility, and proactive data quality monitoring.
Post Grid/Carousel 4-col (4:3)
the excerpts here are clipped to ensure same height. autoplay/autoscrolling
- Apple uses DataHub to manage machine learning metadata, custom entities, and AI…
- Visa replaced its custom catalog with DataHub, using API-powered metadata to scale…
- Slack collapsed 6 years of metadata complexity into 3 days of progress…
- Deutsche Telekom deployed DataHub to simplify discovery, resolve pipeline issues faster, and…
- Chime uses DataHub Cloud to unify producers and consumers, enabling shared ownership,…
Full Width Post Grid/Carousel (4:3)
Sadly, we’re maxed out at 8 columns with this existing WP block, unless you edit the “edit individually” columns link, which is really small
- Apple uses DataHub to manage machine learning metadata, custom entities, and AI…
- Visa replaced its custom catalog with DataHub, using API-powered metadata to scale…
- Slack collapsed 6 years of metadata complexity into 3 days of progress…
- Deutsche Telekom deployed DataHub to simplify discovery, resolve pipeline issues faster, and…
- Chime uses DataHub Cloud to unify producers and consumers, enabling shared ownership,…
- Pinterest replaced rigid workflows with DataHub’s flexible metadata platform—powering custom integrations, intuitive…
- Foursquare replaced fragmented systems with a flexible metadata platform using DataHub, boosting…
- Learn how Airtel scaled data governance and discovery across 30+ PB and…
- Etsy retired a 9-year-old catalog, improved data discovery, and built a governance…
- Optum built data mesh on DataHub to enable decentralized discovery, automate workflows,…
- Discover how Adevinta built a centralized data catalog using DataHub to simplify…
- Checkout.com uses DataHub’s Actions Framework to trigger real-time PII masking, automate dataset…
- With DataHub Cloud, DPG Media reduced data sprawl, enforced governance, and saved…
- INDUSTRY Financial Services SIZE 500+ employees DATA STACK AWS, Kubernetes, Kafka, RDBMS,…
- HashiCorp reduced ad hoc inquiries to near zero by centralizing documentation, ownership,…
- Hurb uses DataHub to streamline ingestion, automate lineage, and centralize discovery across…
- With DataHub, KPN created a scalable data mesh with full lineage and…
- MediaMarktSaturn used DataHub to streamline discovery and automate access provisioning for 50K+…
- Miro uses DataHub Cloud to track lineage, surface SLAs, and empower both…
- With DataHub, MYOB automated schema-change alerts and reduced breaking changes to near…
- Uken Games used DataHub to identify 40% of unused tables, reduce storage…
- From deployment pain to platform success: Learn how Wolt uses DataHub to…
- Zynga uses DataHub to unify metadata, track lineage, monitor quality, and streamline…
Portfolio Grid/Carousel method
Grid, with a post title
Zynga Levels Up Data Management
Advanced Post Query – Carousel Experiment
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Context Graph vs Knowledge Graph: Same Shape, Different Scope
Context graphs and knowledge graphs share the same shape. The real difference is scope and grounding, and it matters more than the vocabulary.
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What Is a Metadata Knowledge Graph? A DataHub Definition
A metadata knowledge graph connects your data assets, pipelines, and meaning. Here’s what it is and why DataHub calls it a context graph.
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How to Implement an Enterprise Context Layer: A Phased Guide for Real Data Estates
A phased, practitioner’s guide to implementing an enterprise context layer on the metadata infrastructure you already have.
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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 how it differs from a data catalog.
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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 an infrastructure problem, and what production agents actually require.
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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 the retrieval engine, and what it takes to build one regulators trust.
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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 one. Here’s what enterprises actually need.
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Context Layer vs Semantic Layer
Context layer vs semantic layer: What each does, how they relate, and why both need context management infrastructure.
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RAG vs Context Management
RAG is a retrieval pattern. Context management is the infrastructure that makes it work at scale. Learn the difference.
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How to Use the DataHub Cloud Value Estimator
Use this business value estimator to build a credible business case, grounded in third-party research, for what DataHub Cloud can deliver for your organization.…
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Launching our Connector to GCP Knowledge Catalog
DataHub’s GCP Knowledge Catalog connector supports bidirectional sync across Vertex AI, BigQuery, Pub/Sub, and more. Now in v1.5.0.2.
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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 entries. Available in v0.14.1.
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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 the role involves and why data engineers are a natural fit.
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Context Engineering vs Prompt Engineering
Context engineering vs prompt engineering: What changed, what’s different, and the infrastructure layer most teams are missing.
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Context Engineering vs Context Management
Context engineering optimizes one agent. Context management scales trusted context across all of them. See how.
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Context Window Optimization
Context window optimization techniques for AI agents, plus why upstream context quality determines the ceiling.
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Context Management for Data Analysts
AI can write SQL. Context management is the new skill set that keeps data analysts indispensable.
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Agents, Apps & the Art of Extension: March 2026 Town Hall Highlights
Ask DataHub in production, micro frontends, Agent Context Kit, and Skills Registry updates—all from the March 2026 DataHub town hall
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What Is a Context Window?
Learn what a context window is, how tokens work, why context limits matter for AI agents, and what it takes to manage context at…
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Context Management Strategies That Actually Scale
Context management strategies that work at scale require organizational infrastructure, not just better prompts. Learn how to build one.
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Context Management Tools in 2026
Four types of tools claim the “context management” label. Here’s how the landscape breaks down and what to evaluate.
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What Is a Context Graph and Why Does It Matter for AI Agents?
A context graph unifies metadata and knowledge into one network AI agents can act on. Here’s what it takes to build one.
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AI-Generated Documentation and Context Propagation
AI-generated documentation solves cold-start. Context propagation makes it stick. See how the two work together.
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Supercharging Snowflake Agents with DataHub Context
Snowflake agents are only as smart as the context they have. Learn how DataHub’s Agent Context Kit adds business definitions, lineage, and data quality.











