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# DataHub

## Sitemaps
[XML Sitemap](https://datahub.com/sitemap_index.xml): Includes all crawlable and indexable pages.

## Blog
- [The Best Context Management Platforms for AI Agents in 2026](https://datahub.com/blog/best-context-management-platform/): Compare the best context management platforms for AI agents in 2026: enterprise context platforms, data catalogs, and open-source options.
- [Azure Data Lineage That Doesn’t Stop at the Azure Boundary](https://datahub.com/blog/azure-data-lineage/): Azure data lineage shouldn't stop at the Microsoft edge. See how DataHub adds column-level depth and unifies it across your whole stack.
- [Data Lineage in ETL: What It Takes to Be Useful in Production](https://datahub.com/blog/what-is-data-lineage-in-etl/): Lineage in ETL only works when it meets three conditions: column-level, cross-platform, and captured at runtime. Here's why.
- [Data Lineage vs. Data Observability: How They Differ and Why You Need Both](https://datahub.com/blog/data-lineage-vs-data-observability/): Data lineage vs. data observability: what each does, where they overlap, and why running both on the same graph collapses the comparison.
- [Databricks Data Lineage: From Unity Catalog to Your Entire Stack](https://datahub.com/blog/databricks-data-lineage/): Databricks data lineage is automatic within Unity Catalog. See how to extend it across your entire stack, without replacing UC.
- [Build with DataHub: The Agent Hackathon is Open Now](https://datahub.com/blog/build-with-datahub-agent-hackathon/): We're launching DataHub's first agent-focused hackathon. Build with DataHub: The Agent Hackathon runs July 6 through August 10. Five weeks, $20,500 in prizes, four challenge categories. 
- [BigQuery Data Lineage: From the Google Cloud Console to Your Entire Stack](https://datahub.com/blog/bigquery-data-lineage/): BigQuery data lineage is scoped to Google Cloud by design. See how to trace it cross-platform for impact analysis, AI agents, and cost.
- [AWS Data Lineage: From Native Capture to Your Entire Stack](https://datahub.com/blog/aws-data-lineage/): AWS data lineage is captured service by service. See how to unify it across your whole stack, without replacing native AWS tooling.
- [Snowflake Data Lineage: What Native Tools Track and Where They Stop](https://datahub.com/blog/snowflake-data-lineage/): Snowflake data lineage stops at the warehouse edge. See why cross-platform lineage matters for impact analysis, AI agents, and cost.
- [Why AI Agents Need Human-Validated Semantic Context](https://datahub.com/blog/ai-agents-human-validated-context/): Auto-generated context isn't enough. See why trusted AI answers require human-in-the-loop validation — and how DataHub makes it scale.
- [Data Pipeline Lineage: Seeing Inside Your Pipelines, Not Just Around ThemData Pipeline Lineage](https://datahub.com/blog/data-pipeline-lineage/): Data pipeline lineage shows what happens inside your pipelines, not just which tables they touched. Here's how DataHub models it.
- [No-Code Automation for Metadata Enrichment: How Modern Catalogs Stay Current at Scale](https://datahub.com/blog/no-code-automation-for-metadata-enrichment/): No-code automation for metadata enrichment fixes the math problem of manual documentation. How it works, governed, at scale.
- [Context Management Is the Missing Piece in the Agentic AI Puzzle](https://datahub.com/blog/context-management/): Context management gives AI agents secure, reliable access to enterprise data. Learn what it is and how to implement it.
- [Introducing DataHub Cloud 2.0](https://datahub.com/blog/datahub-cloud-2-0/): Analytics agents don't fail because of bad models. They fail because of bad context. DataHub Cloud 2.0 is the context platform built to change that.
- [BCBS 239 Data Lineage: Why Compliance and AI Readiness Are the Same Investment](https://datahub.com/blog/bcbs-239-data-lineage/): Most banks still aren't BCBS 239 compliant. The lineage that satisfies the regulator is the same infrastructure that governs your AI.
- [Data Lineage vs. Data Provenance: What’s the Difference?](https://datahub.com/blog/data-lineage-vs-data-provenance/): Data lineage tracks how data moves and transforms. Data provenance tracks where it came from and who handled it. Here's how they differ.
- [Data Lineage: What It Is and Why It Matters](https://datahub.com/blog/data-lineage-what-it-is-and-why-it-matters/): Data lineage tracks where data comes from, how it transforms, and where it ends up. Learn why it matters and how to implement it across your stack.
- [Data Lineage Mapping: Why Manual Maps Fail at the Moment You Need Them](https://datahub.com/blog/data-lineage-mapping/): Manual lineage maps drift between refreshes. Here's why automated, column-level data lineage mapping is the only version that holds up.
- [Data Lineage for Compliance: From Audit Prep to Operational Evidence](https://datahub.com/blog/data-lineage-for-compliance/): How data lineage for compliance turns audits into a byproduct, not a project. Coverage of BCBS 239, GDPR, HIPAA, SOX, and the EU AI Act.
- [What Is Data Lineage in Data Governance?](https://datahub.com/blog/what-is-data-lineage-in-data-governance/): Data lineage operationalizes data governance, propagating classifications and tags across the data estate
- [​Context to Action: May 2026 Town Hall Highlights](https://datahub.com/blog/datahub-may-2026-town-hall-highlights-context-to-action/): Ask DataHub in production, micro frontends, Agent Context Kit, and Skills Registry updates—all from the March 2026 DataHub town hall
- [Data Pipeline Optimization: The Three Levers Compute Tuning Can’t Touch](https://datahub.com/blog/data-pipeline-optimization/): Most data pipeline optimization tops out at compute tuning. Here's the next lever: dependency-aware optimization through lineage.
- [Context Layer Components for AI Agents: What It Takes to Build Each One](https://datahub.com/blog/context-layer-components/): Gartner names three context layer components. A CTO's view of what it takes to build each one in production for AI Agents.
- [Data Lineage Examples Every Data Team Runs Into](https://datahub.com/blog/data-lineage-examples/): See how data lineage works in practice through real examples, from debugging dashboards to proving compliance and grounding AI agents.
- [End-to-End Data Lineage: What the Term Means and What It Actually Takes](https://datahub.com/blog/end-to-end-data-lineage/): End-to-end data lineage gets claimed everywhere and delivered rarely. Here's what the term actually requires and how to test yours.
- [Open Source Data Lineage: Standards, Tools, and When You’ve Outgrown Them](https://datahub.com/blog/open-source-data-lineage/): An honest look at open source data lineage: the OpenLineage standard, the tool landscape, and how to pick one that won't strand you later.
- [Data Lineage vs Data Catalog: Two Questions, One Metadata Graph](https://datahub.com/blog/data-lineage-vs-data-catalog/): Data lineage vs data catalog answer different questions. See why treating them as separate tools creates more problems than it solves.
- [What Is Metadata Lineage? (And Why It’s Not Quite the Same as Data Lineage)](https://datahub.com/blog/metadata-lineage/): Metadata lineage means two things: Data lineage by another name, and the audit trail of metadata itself. Why both matter for AI governance.
- [Context Layer for Snowflake: Extending Trustworthy Context Beyond the Warehouse](https://datahub.com/blog/context-layer-for-snowflake/): Snowflake gives you context inside the warehouse. A context layer for Snowflake extends it across every system your data and AI touch.
- [The Benefits of Data Lineage: From Table to Column to Unified Platform](https://datahub.com/blog/data-lineage-benefits/): Data lineage benefits depend on resolution and where lineage lives. See what table-level, column-level, and unified-platform lineage deliver.
- [DataHub and ClickHouse](https://datahub.com/blog/clickhouse_partnership/): Announcing partnership Datahub Clickhouse Connector
- [Data Lineage Tools in 2026: Where Lineage Lives in Your Stack](https://datahub.com/blog/data-lineage-tools/): Most "best data lineage tools" lists rank vendors. This guide maps where lineage lives in your stack and how to choose.
- [Data Lineage for Machine Learning: Why Reliable ML Lives Upstream](https://datahub.com/blog/data-lineage-for-ml/): Most ML failures trace to upstream data, not model drift. Data lineage for machine learning is how you debug, govern, and trust ML.
- [Introducing DataHub Cloud v1.1.0](https://datahub.com/blog/introducing-datahub-cloud-v1-1-0/): DataHub Cloud v1.1.0 introduces the Context Management Platform: auto-generated business context, SME validation workflows, and agent activation across Snowflake, Databricks, Claude, and more.
- [Announcing the DataHub Context Platform](https://datahub.com/blog/announcing-datahub-context-platform/): Analytics agents don't fail because of bad models. They fail because of bad context. DataHub Cloud 2.0 is the context platform built to change that.
- [Column-Level Lineage: What It Is and Why Cross-Platform Coverage Matters](https://datahub.com/blog/column-level-lineage-comes-to-datahub/): Column-level lineage traces every field from source to dashboard. Learn how it works and why cross-platform coverage matters.
- [SQL Lineage: How DataHub Extracts Column-Level Lineage from Queries](https://datahub.com/blog/extracting-column-level-lineage-from-sql/): How DataHub extracts column-level SQL lineage with a schema-aware parser built on SQLGlot. Inside the architecture and design tradeoffs.
- [AI Agent Onboarding: The Missing Discipline Behind Agents That Actually Work](https://datahub.com/blog/ai-agent-onboarding/): AI agent onboarding is the missing discipline behind production-ready agents. Why context engineering can't do the job alone.
- [​Trusted Context for Talk-to-Data: April 2026 Town Hall Highlights](https://datahub.com/blog/trusted-context-for-talk-to-data-april-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
- [Context Ownership: A Shared Operating Model](https://datahub.com/blog/context-ownership/): Context ownership can't sit with one team. Here's how data, analyst, and governance functions share it across a context platform.
- [How to Talk to Your Data (and Actually Get the Right Answer)](https://datahub.com/blog/how-to-talk-to-your-data/): Talk-to-data agents fail without context. Here's what an LLM actually needs to query your warehouse and return the right answer.
- [How to Build a Context Layer for AI: A Practitioner’s Guide](https://datahub.com/blog/how-to-build-a-context-layer/): Building a context layer for AI starts with what you already have. The four capabilities every production-ready implementation needs.
- [AI Agent Memory: Why Memory Quality Is a Data Problem (Not an Architecture Problem)](https://datahub.com/blog/ai-agent-memory/): AI agent memory architecture is mature. Memory quality isn't. Here's why governed context is the prerequisite for agent memory you can trust.
- [Continuous Context: Why Your AI Documentation Is Already Lying to You](https://datahub.com/blog/continuous-context/): AI agents can't compensate for stale docs the way humans can. Continuous context is the missing maintenance layer. Here's what it looks like.
- [Context Platform ROI: The Real Cost (and the Hidden One You’re Already Paying)](https://datahub.com/blog/context-platform-roi/): Context platform ROI, measured. IDC's five categories of hidden spend most organizations are already paying without knowing it.
- [Data Context Inventory: The Prerequisite Most AI Projects Skip](https://datahub.com/blog/data-context-inventory/): A data context inventory is the audit step most AI projects skip. Map your context across six dimensions before agents go live.
- [The Five Common Context Problems Data Teams Face (and How to Solve Them)](https://datahub.com/blog/common-context-problems-data-teams-face/): The five context problems breaking AI agents in production, and how a context platform fixes each without duplicating RAG pipelines.
- [Context Preparation vs. Data Preparation: Why Agentic AI Needs Both](https://datahub.com/blog/context-preparation-vs-data-preparation/): Data prep made data usable for analysts. Context preparation makes it usable for agents. Why both matter, and why most enterprises have only done one.
- [Business Context vs. Technical Metadata: Why the Gap Breaks AI Agents](https://datahub.com/blog/business-context-vs-technical-metadata/): Technical metadata says what data is. Business context says what it means. Learn why that gap breaks AI agents.
- [AI-Ready Context: Why Your Agents Don’t Need More Data, They Need to Understand It](https://datahub.com/blog/ai-ready-context/): AI-ready data isn't enough. Agents need AI-ready context: the definitions, runbooks, and institutional knowledge that give structured data meaning.

## Pages
- [Events](https://datahub.com/events/)
- [Context 2026](https://datahub.com/context/): Join CONTEXT 2026, a half-day virtual summit on November 4 for data practitioners building the metadata and AI context layer. Deep dives on governance, lineage, and AI-ready data stacks.
- [DataBricks Partner Page](https://datahub.com/partners/databricks/): Context for the Leading AI Data Cloud
- [Context Management Platform](https://datahub.com/products/context-platform/): A data catalog indexes structured metadata about data assets — schemas, lineage, ownership, quality metrics — and delivers it to humans through a portal interface. A context platform unifies that metadata with the unstructured organizational knowledge that gives it meaning: runbooks, decision logs, business glossaries, and policies. The result is a unified context layer that serves both humans and AI agents. The distinction is architectural, not a checklist of features: A data catalog is a tool; a context platform is infrastructure. For a full breakdown of where the two diverge, see our post on context platform vs. data catalog.
- [Context Management Hub](https://datahub.com/learn/context-management/): Context management is the organization-wide capability to reliably deliver the most relevant data to AI context windows, enabling governed, enterprise-scale deployment of agents. It covers how context is sourced, enriched, maintained, and delivered across every AI application in an organization — not just within individual pipelines or tools. Without it, agents operate on fragmented, stale, or ungoverned information, and production AI initiatives stall. Read our article Context Management is the Missing Piece in the Agentic AI Puzzle for a detailed overview.
- [Learn](https://datahub.com/learn/)
- [Champions](https://datahub.com/community/champions/)
- [The ROI of DataHub Cloud](https://datahub.com/roi/): Independent research from IDC quantifies what DataHub Cloud customers already know: context management pays. Here's the proof.
- [AWS Partner Page](https://datahub.com/partners/aws/): Get better returns on your AWS investment with DataHub
- [Google Cloud Partner Page](https://datahub.com/partners/google-cloud/)
- [Partners](https://datahub.com/partners/): DataHub ships deep, validated integrations with each strategic partner — bidirectional metadata, column-level lineage, and governance that stay in sync with your warehouse, available through every partner's marketplace.
- [DataHub Town Halls](https://datahub.com/community/datahub-town-halls/)
- [DataHub Office Hours](https://datahub.com/community/office-hours/)
- [GCP-Free-Trial](https://datahub.com/google-cloud-free-trial/): Start your DataHub Cloud free trial. Connect BigQuery to discover insights, and deploy data quality checks—all with guided implementation support included.
- [Snowflake Partner Page](https://datahub.com/partners/snowflake/): DataHub's Snowflake integration is designed for reliability and ease of use. As an official partner, we maintain deep compatibility and deliver updates aligned with Snowflake's evolving capabilities.
- [Talk to Sales](https://datahub.com/talk-to-sales/): Ready to take the next step with DataHub? Share a few details and we’ll connect you with the right DataHub expert to discuss pricing, deployment options, and what it looks like to move forward.
- [AI Data Management Platform](https://datahub.com/products/ai-data-management/): IDC,  "The Business Value of DataHub Cloud," March 2026, sponsored by DataHub
- [Data Lineage](https://datahub.com/products/data-lineage/): IDC,  "The Business Value of DataHub Cloud," March 2026, sponsored by DataHub
- [HTML-SiteMap](https://datahub.com/html-sitemap/)
- [Protected: FAQ example](https://datahub.com/faq-example/): Because we all have the capacity to do justice and show mercy; to treat others with dignity and respect; and to rise above what divides us and come together to meet those challenges we can't meet alone. But the remarks that have caused this recent firestorm weren't simply controversial. I'll help our auto companies re-tool, so that the fuel-efficient cars of the future are built right here in America.
- [Blog](https://datahub.com/blog/)
- [Zoom](https://datahub.com/zoom/): Discover the latest metadata & AI insights, product updates, events, and more in our resource library.
- [General Terms and Conditions for Fully Hosted Services](https://datahub.com/saasterms/): THESE GENERAL TERMS AND CONDITIONS FOR FULLY HOSTED SERVICES GOVERN CUSTOMER’S SUBSCRIPTION TO THE SERVICES DEFINED BELOW.
- [DATAHUB AI FEATURES](https://datahub.com/aiterms/): Last Updated:  01 May 2026
- [Data Processing Addendum](https://datahub.com/dpa/): Last Updated: 22 June 2026
- [News](https://datahub.com/news/): Latest News and Press Releases
- [DataHub Careers](https://datahub.com/datahub-careers/): Data is powering AI. But without context, even the best models fall short.
- [DataHub – AI & Data Context Management](https://datahub.com/)
- [Thank You Demo](https://datahub.com/thank-you-demo/): Thank you for expressing interest in DataHub!
- [Demo](https://datahub.com/demo/): Share a few details about your goals, and we’ll be in touch to schedule a personalized walkthrough tailored to your data environment, use cases, and roadmap.
- [Share Your Journey](https://datahub.com/community/share-your-journey/): DataHub was built alongside a passionate community of data engineers, stewards, and leaders who care deeply about trustworthy data. On this page, we invite you to share your unique DataHub journey so others can learn from your experience implementing modern metadata management.
- [Guild](https://datahub.com/guild/): Celebrating community members that have gone above and beyond to contribute to the collective success of DataHub
- [Slack](https://datahub.com/slack/)
- [Thank You](https://datahub.com/thank-you/): Thank you for expressing interest in DataHub!
- [Product Tour](https://datahub.com/product-tour/): In this short tour, you’ll see how DataHub Cloud helps data teams break down silos, build trust in data, and accelerate AI and analytics, all from one unified platform.
- [Why DataHub Cloud](https://datahub.com/products/why-datahub-cloud/)
- [Data Discovery](https://datahub.com/products/data-discovery/): With the right data discovery tool, data teams reclaim the hours wasted hunting for data they already own. Businesses deploy data discovery platforms like DataHub to solve critical bottlenecks, enabling users to:

## Customer Stories
- [Super Technologies turns 600 quality checks into company-wide confidence with DataHub and Snowflake](https://datahub.com/customer-stories/super-technologies/): Learn how Super Technologies scaled data quality and discovery across 150+ data professionals, cutting KPI lookup time from significant and lengthy manual effort to seconds with DataHub Cloud.
- [Pinterest Powers its #1 AI Agent with DataHub Context](https://datahub.com/customer-stories/pinterest/): Learn how Pinterest solved the context problem behind text-to-SQL and shipped an AI analytics agent with 10x the usage of any other internal tool.
- [Netflix Reimagines Discovery and Governance at Scale](https://datahub.com/customer-stories/netflix/): With DataHub, Netflix empowers teams to define and manage metadata through self-serve workflows, improving flexibility and governance.
- [Apple’s Machine Learning Data Gets Tuned Up](https://datahub.com/customer-stories/apples-machine-learning-data-gets-tuned-up/): Apple uses DataHub to manage machine learning metadata, custom entities, and AI governance across a fast-evolving data landscape.
- [Visa Scales Data Governance](https://datahub.com/customer-stories/visa/): Visa replaced its custom catalog with DataHub, using API-powered metadata to scale governance, improve quality, and support AI workflows across global teams.
- [Slack Solves 6 Years of Metadata Complexity in 72 Hours](https://datahub.com/customer-stories/slack/): Slack collapsed 6 years of metadata complexity into 3 days of progress with DataHub—unlocking extensible discovery, lineage, and governance across teams.
- [Deutsche Telekom Calls the Experts to Streamline Data Discovery](https://datahub.com/customer-stories/deutsche-telekom/): Deutsche Telekom deployed DataHub to simplify discovery, resolve pipeline issues faster, and power AI platforms with metadata context.
- [Chime’s Data Now Works in Harmony With Their Teams](https://datahub.com/customer-stories/chime/): Chime uses DataHub Cloud to unify producers and consumers, enabling shared ownership, lineage visibility, and proactive data quality monitoring.
- [Foursquare’s Data Stack Gets Squared Away](https://datahub.com/customer-stories/foursquare/): Foursquare replaced fragmented systems with a flexible metadata platform using DataHub, boosting developer efficiency and governance.
- [Airtel Expands Their Data Horizon](https://datahub.com/customer-stories/airtel/): Learn how Airtel scaled data governance and discovery across 30+ PB and 10K+ jobs with DataHub as its metadata management backbone.
- [Notion Takes Note on Data Chaos](https://datahub.com/customer-stories/notion/): Notion scales metadata management with DataHub Cloud, improving impact analysis, self-serve discovery, and GDPR compliance.
- [Etsy Crafts a Data Discovery Masterpiece](https://datahub.com/customer-stories/etsy/): Etsy retired a 9-year-old catalog, improved data discovery, and built a governance foundation using DataHub.
- [Optum Opts For a Scalable Data Mesh](https://datahub.com/customer-stories/optum/): Optum built data mesh on DataHub to enable decentralized discovery, automate workflows, and streamline access across petabyte-scale healthcare data.
- [Adevinta Thinks Local While Taking Their Data Global](https://datahub.com/customer-stories/adevinta/): Discover how Adevinta built a centralized data catalog using DataHub to simplify discovery, manage metadata, and improve collaboration.
- [Checkout.com Gets Real-Time With Its Data](https://datahub.com/customer-stories/checkout-com/): Checkout.com uses DataHub’s Actions Framework to trigger real-time PII masking, automate dataset deprecation, and improve auditability.
- [DPG Media Entertains a Modern Data Catalog Solution, and Saves](https://datahub.com/customer-stories/dpg-media/): With DataHub Cloud, DPG Media reduced data sprawl, enforced governance, and saved 25% monthly on Snowflake storage and compute.
- [Funding Circle Turns Around Their Metadata Management](https://datahub.com/customer-stories/funding-circle-turns-around-their-metadata-management/): Funding Circle, a lending platform that has helped over 140,000 small businesses secure loans, faced significant barriers to achieving self-service data capabilities.
- [HashiCorp Streamlines Their Data Discovery Chaos ](https://datahub.com/customer-stories/hashicorp-streamlines-their-data-discovery-chaos/): HashiCorp reduced ad hoc inquiries to near zero by centralizing documentation, ownership, and lineage with DataHub.
- [Hurb Arrives at Their Destination: A Single Source of Truth Across a Growing Data Stack](https://datahub.com/customer-stories/hurb/): Hurb uses DataHub to streamline ingestion, automate lineage, and centralize discovery across its growing data stack.
- [KPN Readies Their Data for the Future](https://datahub.com/customer-stories/kpn/): With DataHub, KPN created a scalable data mesh with full lineage and support for internal and external data use.
- [MediaMarktSaturn Maximizes Data Access While Minimizing Customer Friction](https://datahub.com/customer-stories/mediamarktsaturn/): MediaMarktSaturn used DataHub to streamline discovery and automate access provisioning for 50K+ employees across 30+ data domains.
- [Miro Establishes Trust Through Reliable Data Products](https://datahub.com/customer-stories/miro/): Miro uses DataHub Cloud to track lineage, surface SLAs, and empower both analysts and engineers with clear data product visibility.
- [MYOB Balances the Books on Data Reliability](https://datahub.com/customer-stories/myob/): With DataHub, MYOB automated schema-change alerts and reduced breaking changes to near zero—even as Snowflake usage grew 4x.
- [Uken Games Reduces Infrastructure Waste by 40%](https://datahub.com/customer-stories/uken-games/): Uken Games used DataHub to identify 40% of unused tables, reduce storage waste, and make self-serve analytics faster and more reliable.
- [Wolt Finds their Perfect-Fit Metadata Platform](https://datahub.com/customer-stories/wolt/): From deployment pain to platform success: Learn how Wolt uses DataHub to track data lineage, improve discoverability, and support legal compliance at scale.
- [Zynga Levels Up Data Management ](https://datahub.com/customer-stories/zynga/): Zynga uses DataHub to unify metadata, track lineage, monitor quality, and streamline data ops across 100+ games and 35B+ daily records.

## Guides
- [Harnessing Unstructured Data for AI Innovation: 2026 BARC Study](https://datahub.com/guides/harnessing-unstructured-data-for-ai-innovation-2026-barc-study/): Survey data from 250 IT and data leaders exposes the gap between AI confidence and the context management infrastructure production-scale agentic AI demands.
- [From Compliance Gap to Audit-Ready: A Faster Path for Banks](https://datahub.com/guides/audit-ready-data-governance/): Most financial institutions can defend a fraction of the fields examiners ask about. The rest lives in tribal knowledge, stale spreadsheets, and manual effort that can't scale. DataHub's FinServ Compliance QuickStart shows how mid-tier and regional banks are closing that gap, in weeks instead of quarters.
- [The State of Context Management in 2026](https://datahub.com/guides/2026-context-management-report/): Survey data from 250 IT and data leaders exposes the gap between AI confidence and the context management infrastructure production-scale agentic AI demands.
- [BCBS 239 Compliance and Beyond](https://datahub.com/guides/bcbs-239-compliance-and-beyond/): This guide shows how forward-looking banks can go beyond box-ticking compliance. By aligning BCBS 239’s principles with DataHub’s AI & data context platform, financial institutions can strengthen resilience, accelerate decision-making, and lay the foundation for trustworthy AI adoption.
- [Context: The Missing Link Between Your Data Stack and AI Success](https://datahub.com/guides/context-missing-link/): Discover proven strategies for building AI-ready data foundations, implementation blueprints for DataHub Cloud, and a detailed framework to assess your organization's context maturity and choose between Core vs. Cloud solutions.
- [7 Reasons to rethink your Data Catalog](https://datahub.com/guides/7-reasons-to-rethink-your-data-catalog/): Traditional data catalogs were designed for an erstwhile era of data management. As your organization evolves, has your metadata approach kept pace?

## Resource Articles
- [Product Demos](https://datahub.com/resources/product-demos/)
- [Context On-Demand Webinars](https://datahub.com/resources/context/): Revisit every session from CONTEXT 2025: insights from the world’s top data and AI leaders.
- [DataHub vs Atlan | The Essential Metadata Platform Comparison Guide](https://datahub.com/resources/datahub-vs-atlan/): Compare DataHub vs Atlan across scalability, governance, AI readiness, and extensibility. See why modern enterprises choose DataHub as their metadata platform.
- [AI-Ready Data: What it Is and the 5 Pillars You Need to Know](https://datahub.com/resources/ai-ready-data/): AI-ready data is data that is prepared, structured, and governed in a way that enables AI systems to consume, learn from, and act on it at scale. It is complete, high-quality, contextualized, and optimized data not just for human analysis but for machine learning, inference, and automation.
- [DataHub MCP Server: Unlocking AI Agent Potential with Enterprise Data Context](https://datahub.com/resources/datahub-mcp-server-overview/): The Model Context Protocol (MCP) and the DataHub MCP Server solve this problem. Together, they give AI agents standardized, real-time access to rich metadata that provides the full context of enterprise data, including its meaning, its behavior, and its rules.
- [Modern Metadata Platforms](https://datahub.com/resources/modern-metadata-platforms/): Traditional catalogs struggle with the volume, velocity, and variety of metadata in modern data ecosystems, leading to incomplete coverage and performance issues.
- [DataHub Announces Support for Model Context Protocol](https://datahub.com/resources/datahub-mcp-server/): AI agents are unlocking a powerful new way to explore and interact with data. By integrating generative AI applications into DataHub via the Model Context Protocol (MCP), teams can now query, understand, and act on data more naturally, right where they work.

## News
- [DataHub Launches Breakthrough Release That Gives Analytics Agents Trusted Context, Pushing Accuracy Levels Beyond 90%](https://datahub.com/news/datahub-launches-breakthrough-release-that-gives-analytics-agents-trusted-context-pushing-accuracy-levels-beyond-90/): PALO ALTO, Calif., May 28, 2026 – DataHub, the leading context platform company, today introduced a major new release of DataHub Cloud that can ingest, structure, improve and serve trusted context to analytics agents, dramatically increasing their accuracy and reliability in production. DataHub Cloud v1 serves as a context layer that sits between analytics agents, like Databricks Genie and Snowflake Intelligence, and enterprise data from data stores, like data warehouses and data lakes, to give agents trusted context to get analytics right and smarter with every query. 
- [DataHub and Google Deepen Collaboration in Unifying Multi-Platform Context and Accelerating Trusted AI Deployments](https://datahub.com/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 team leaders on the topic of data context management for AI agents. See full findings.
- [DataHub Releases State of Context Management Report](https://datahub.com/news/datahub-releases-state-of-context-management-report/): DataHub commissioned independent research firm TrendCandy to survey 250 IT and data team leaders on the topic of data context management for AI agents. See full findings.
- [DataHub Joins Snowflake Open Semantic Interchange (OSI)](https://datahub.com/news/datahub-joins-snowflake-open-semantic-interchange/): DataHub’s open source metadata platform brings governance and discoverability to the emerging universal semantic data framework
- [DataHub Hires Product & Engineering Leaders](https://datahub.com/news/datahub-hires-product-engineering-leaders/): DataHub welcomes new executives to drive innovations for open-source community and enterprise customers.
- [DataHub Announces Key Executive Hires](https://datahub.com/news/datahub-announces-key-executive-hires/): DataHub, the leading open source metadata platform, has secured $35 million in Series B funding led by Bessemer Venture Partners to address the critical "missing context" challenge in enterprise AI. The investment will accelerate DataHub's mission to enable AI systems to autonomously and safely work with organizational data assets, providing the context machines need to understand data lineage, quality, and semantics.
- [DataHub Series B Announcement](https://datahub.com/news/series-b-announcement/): DataHub, the leading open source metadata platform, has secured $35 million in Series B funding led by Bessemer Venture Partners to address the critical "missing context" challenge in enterprise AI. The investment will accelerate DataHub's mission to enable AI systems to autonomously and safely work with organizational data assets, providing the context machines need to understand data lineage, quality, and semantics.
- [Acryl Data Strengthens Executive Team to Scale Go-to-Market Capabilities](https://datahub.com/news/acryl-data-strengthens-executive-team/): Acryl Data has appointed Lachlan Brown as Chief Revenue Officer and Satprit Duggal as Chief Marketing Officer, reinforcing the company's commitment to accelerating go-to-market efforts. These strategic hires come amid growing demand for DataHub, Acryl Data's industry-leading open source metadata platform for data discovery, observability, and governance.
- [Acryl Data Announces the Inaugural Metadata and AI Summit 2024](https://datahub.com/news/acryl-data-inaugural-metadata-and-ai-summit-2024/): Acryl Data, the company behind the leading metadata platform DataHub, announces the Metadata and AI Summit 2024, scheduled for October 29-30, 2024. This two-day virtual event will bring together over 2,500 data professionals, machine learning engineers, analysts, and thought leaders to discuss cutting-edge AI applications and metadata-driven solutions that are shaping the future of enterprise AI.
- [Acryl Data Re-Imagines Metadata Management With $9 Million in Seed Funding](https://datahub.com/news/acryl-data-seeds-9m-for-metadata-management/): Acryl Data Raises $9 Million from 8VC, LinkedIn and Insight Partners
Today, Acryl Data also announced that it has raised $9 million in seed funding led by 8VC. LinkedIn and Insight Partners also participated.

## Events
- [Databricks Data + AI Summit 2026](https://datahub.com/events/databricks-data-ai-summit-2026/): Genie is live. Is your context ready? Come find out what it takes to make your analytics agent accurate.
- [Snowflake Summit 2026](https://datahub.com/events/snowflake-summit-2026/): Add business context, governance, and lineage to your Snowflake Data so teams can move faster with confidence.
- [Gartner Data & Analytics Summit London 2026](https://datahub.com/events/gartner-data-analytics-summit-london-2026/): London, U.K. | May 11 - 13, 2026 | Booth 201
- [Google Cloud Next 2026](https://datahub.com/events/google-cloud-next-2026/): Las Vegas, NV | April 22 - 24, 2026 | Booth 3201
- [Gartner Data & Analytics Summit Orlando 2026](https://datahub.com/events/gartner-data-analytics-summit-orlando-2026/): Orlando, FL | March 9 -11, 2026 | Booth 906

## Webinars
- [Talk to your Data on AWS with LangChain Deep Agents and DataHub](https://datahub.com/webinars/talk-to-your-data-on-aws-with-langchain-deep-agents-and-datahub/): Enterprise AI is only as good as the data it reasons over. Getting a fast answer is easy. Getting a correct answer is the hard part.
- [Leading Through the AI Revolution: A Conversation with Jeff Weiner](https://datahub.com/webinars/leading-through-ai-revolution/): Build influence and drive change. Jeff Weiner shares LinkedIn's playbook for data-driven decisions, trusted systems, and preparing teams for AI.
- [FinServ Compliance: Making Regulations Work for You](https://datahub.com/webinars/finserv-compliance-bcbs239/): Turn auditor requests into repeatable workflows. Choose the right lineage level and use compliance as a foundation for AI-readiness.
- [Shift-left Governance: Enabling Engineering Teams to Define Data Policies](https://datahub.com/webinars/shift-left-governance-engineering/): Embed governance into dev workflows without gates. Automate PII tagging to reduce compliance risk and improve audibility starting today.
- [Agentic Workflows in Data Catalog: Beyond “Talk to Your Data”](https://datahub.com/webinars/agentic-workflows-data-catalog/): Automate data governance with AI agents. See Apple's architecture for intelligent catalogs that enrich metadata, enforce policies, and improve quality.
- [Driving Data Catalog Adoption Through Psychology and Design](https://datahub.com/webinars/data-catalog-adoption-psychology-design/): Apply product thinking to governance. Fundamental strategies that make data catalogs intuitive and effortless, not homework—lessons from ICA's rollout.
- [Data Supply Chain Visibility: Practical Benefits of End-to-End Lineage](https://datahub.com/webinars/data-supply-chain-visibility-lineage/): Map lineage for high-risk pipelines first. Understand the impact of change, debug data issues faster, and drive adoption beyond compliance checkboxes.
- [How Foursquare Built a Data Marketplace Using Metadata](https://datahub.com/webinars/foursquare-metadata-data-marketplace/): Build governed data marketplaces using metadata. Combine Iceberg, Spark, and DuckDB with lineage and versioning for enterprise ML at scale.
- [Convergence of Context: Moving Towards a Global Catalog for Netflix](https://datahub.com/webinars/netflix-global-data-catalog/): Learn Netflix's long-term data vision centered on a comprehensive catalog that solves discovery, governance, and lineage at streaming scale.
- [Context for Agents: Fireside Chat with João “Joe” Moura](https://datahub.com/webinars/context-for-ai-agents-fireside-chat/): Assess agent maturity and 2025's most significant developments. Understand enterprise data challenges and how MCP and context platforms scale agents successfully.
- [The Rhythm of AI: Creativity, Metadata, and the Next Wave of Innovation](https://datahub.com/webinars/rhythm-of-ai-creativity-innovation/): Discover how AI augments creativity and why metadata is critical for attribution. See where top VCs spot the next wave of AI-native companies.
- [Unlocking AI’s Potential Through Context Management](https://datahub.com/webinars/unlocking-ai-potential-context-management/): Transform context from bottleneck to advantage. Learn why current approaches create technical debt that prevents enterprise AI from scaling.
- [Metadata Masterclass: Scaling Across Global Enterprises](https://datahub.com/webinars/metadata-masterclass-global-enterprises/): Transform financial services with metadata strategy. Real examples of building data foundations to accelerate projects and enable confident AI adoption.

## Demos
- [Building Reliable Data Foundations for AI: A Live Session with DataHub Cloud](https://datahub.com/demos/bi-weekly-demo/): Experience DataHub Cloud's platform for unified data discovery, observability, and governance across your entire data stack.
- [Automate Data Governance with DataHub Cloud](https://datahub.com/demos/data-governance-with-datahub/): Join our live demo of DataHub Cloud's data governance capabilities. See automated compliance, PII detection, and self-service access workflows.
- [Complete Data Lineage with DataHub Cloud](https://datahub.com/demos/data-lineage-with-datahub/): Watch our on-demand demo of DataHub Cloud's data lineage capabilities. See impact analysis, root cause debugging, and column-level lineage in action.
- [Context-Aware Data Discovery with DataHub Cloud](https://datahub.com/demos/data-discovery-with-datahub/): See DataHub Cloud's data discovery features live. Demo includes natural language search, Slack/Teams integration, automated metadata tools, live Q&A.

## Categories
- [AI Data Management](https://datahub.com/blog/category/ai/)
- [Community](https://datahub.com/blog/category/community/)
- [Context Management](https://datahub.com/blog/category/context-management/)
- [Data Catalog](https://datahub.com/blog/category/data-catalog/)
- [Discovery](https://datahub.com/blog/category/data-discovery/)
- [Governance](https://datahub.com/blog/category/data-governance/)
- [Lineage](https://datahub.com/blog/category/data-lineage/)
- [Observability](https://datahub.com/blog/category/data-observability/)
- [Platform Experience](https://datahub.com/blog/category/platform-experience/)
- [Product Updates](https://datahub.com/blog/category/product-updates/)
- [Team & Culture](https://datahub.com/blog/category/team-culture/)
- [Uncategorized](https://datahub.com/blog/category/uncategorized/)

## About DataHub

DataHub is the AI and data context management platform built on the #1 open-source data catalog. It transforms enterprise metadata into trusted context for humans and AI agents.

DataHub was created by Shirshanka Das and Swaroop Jagadish, who previously built metadata platforms at LinkedIn and Airbnb. The company is headquartered in Palo Alto, California, and is backed by 8VC, LinkedIn, Bessemer Venture Partners, and Next Play Ventures.

## Products

DataHub Cloud is a fully managed enterprise platform with five core capabilities:

- [Discovery](https://datahub.com/products/data-discovery/): AI-powered search and conversational data discovery across 100+ data sources via the Ask DataHub chat agent.
- [Observability](https://datahub.com/products/data-observability/): Proactive data quality monitoring with ML-driven anomaly detection, automated assertions, and incident management.
- [Governance](https://datahub.com/products/data-governance/): Automated compliance workflows, role-based access control, business glossary management, and audit-ready reporting.
- [Lineage](https://datahub.com/products/data-lineage/): Automated column-level lineage across your entire data stack with bidirectional impact analysis.
- [AI and Automation](https://datahub.com/products/ai-data-management/): AI-generated documentation, metadata propagation, intelligent classification, and a hosted MCP Server for AI agent integration.

DataHub Core is the open-source foundation trusted by 3,000+ organizations. DataHub Cloud adds enterprise infrastructure, SLA-backed availability, SOC II compliance, and advanced features on top of Core.

- [Cloud vs Core Comparison](https://datahub.com/products/cloud-vs-core/)
- [Why DataHub Cloud](https://datahub.com/products/why-datahub-cloud/)

## Key Resources

- [What is Metadata Management?](https://datahub.com/blog/what-is-metadata-management/)
- [What is Data Governance?](https://datahub.com/blog/what-is-data-governance/)
- [What is Data Mesh?](https://datahub.com/blog/what-is-data-mesh/)
- [How to Make Data Governance Work in the AI Age](https://datahub.com/blog/how-to-make-data-governance-work-in-the-ai-age/)
- [Data Quality Belongs in the Data Catalog](https://datahub.com/blog/data-quality-belongs-in-the-data-catalog/)
- [Why Data Lineage is Non-Negotiable for Reliable ML](https://datahub.com/blog/data-lineage-for-ml/)
- [7 Reasons to Rethink Your Data Catalog](https://datahub.com/guides/7-reasons-to-rethink-your-data-catalog/)
- [2026 State of Context Management Report](https://datahub.com/guides/2026-context-management-report/)
- [DataHub vs Atlan](https://datahub.com/resources/datahub-vs-atlan/)
- [DataHub MCP Server](https://datahub.com/resources/datahub-mcp-server/)

## Customer Stories

- [Netflix](https://datahub.com/customer-stories/netflix/)
- [Visa](https://datahub.com/customer-stories/visa/)
- [Notion](https://datahub.com/customer-stories/notion/)
- [Slack](https://datahub.com/customer-stories/slack/)
- [Etsy](https://datahub.com/customer-stories/etsy/)
- [Deutsche Telekom](https://datahub.com/customer-stories/deutsche-telekom/)
- [Pinterest](https://datahub.com/customer-stories/pinterest/)
- [Apple](https://datahub.com/customer-stories/apples-machine-learning-data-gets-tuned-up/)
- [Optum](https://datahub.com/customer-stories/optum/)
- [Chime](https://datahub.com/customer-stories/chime/)

## Documentation and Community

- [Documentation](https://docs.datahub.com)
- [GitHub](https://github.com/datahub-project)
- [Slack Community](https://datahub.com/slack/)
- [Blog](https://datahub.com/blog/)
- [Events](https://datahub.com/events/)