As Foursquare grew through multiple acquisitions, they inherited a sprawling and fragmented data ecosystem. This created a severe lack of standardization, leading to costly data duplication, inconsistent lineage, and a discovery process that slowed developer productivity and release cycles.

This success story reveals how Foursquare transformed their data stack by implementing DataHub's flexible, lineage-rich metadata control plane, enabling a developer-first data platform vision.

You will learn how Foursquare:

• Accelerated time-to-discovery and access from days to minutes, dramatically improving developer productivity.
• Adopted DataHub's flexible metadata model to integrate disparate systems (Amazon S3, Redshift, Databricks, and Airflow) without vendor lock-in.
• Automated metadata lineage and established fine-grained access control for sensitive geospatial data.
• Built a scalable control plane that improved visibility into upstream/downstream dependencies, enabling scalable cross-team data reuse.

Download the case study to see how the world's leading geospatial technology company mastered its data complexity.

Netflix's self-built cataloging tool was instrumental early on, but proved limited in scope as their data ecosystem expanded to include real-time pipelines and online stores. The central Data Platform Team became a major bottleneck, shouldering the entire burden of maintaining connectors and struggling to enforce governance policies at scale.

This success story reveals how Netflix moved beyond the limitations of their homegrown solution by choosing DataHub's extensible, self-serve platform.

You will learn how Netflix:

• Improved the productivity of their central data team by enabling self-serve data cataloging across multiple teams.
• Met the unique needs of their complex ecosystem through DataHub's support for custom entity types, ownership models, and properties.
• Strengthened data governance by implementing a central policy engine, offloading the connector development burden from the core team.
• Achieved the scalability and performance required to manage their massive traffic load and data volume.

See the strategy that helped Netflix transition from a restrictive, centralized catalog to an extensible platform that empowers their entire organization.

As the global leader in digital payments, Visa processes massive volumes of sensitive data daily, making scalable data governance essential. However, their custom-built metadata platform became a resource-intensive bottleneck, demanding constant engineering attention and limiting the scalability needed for their vast, distributed data ecosystem.

This success story reveals how Visa overcame three core challenges—managing classifications at scale, capturing high-quality metadata, and reconciling datasets across multiple environments—by implementing DataHub.

You will learn how Visa:

• Used DataHub's API-first platform to create an "invisible catalog" that integrates seamlessly with existing tools (Kafka, Spark, multiple Hadoop clusters, etc.).
• Implemented a Business Attributes Model and Structured Properties to centralize crucial business information owned by data stewards.
• Replaced constant upkeep of bespoke infrastructure, freeing up engineering resources for strategic initiatives.
• Achieved scalable data governance, improved data quality, and enhanced AI capabilities with more reliable data.

See the strategy that helped a global payment network power seamless data governance at scale.

When it comes to scaling AI and real-time data across the enterprise, most data platforms fall short. Traditional data catalogs simply aren't built for the high velocity and governance complexity required by modern intelligent applications.

This guide outlines the 10 core reasons enterprises are choosing DataHub Cloud as the foundation for their intelligent data stack, providing the scalability and control necessary for true AI readiness.

Inside, you will learn about the 10 core enterprise capabilities, including:

• Built for Scale: Supports high data velocity and low-latency needs with 70+ pre-built connectors and real-time metadata updates.
• Native Support for AI/ML Assets: Catalogs the full AI lifecycle, including ML models, training data, and pipelines, enabling true AI observability and lifecycle governance.
• Unified Platform: Unites discovery, observability, and governance in one place, eliminating tool sprawl.
• Accelerated AI-Readiness: Ensures data is usable by AI models through automated freshness checks and AI-based anomaly detection.
• Enterprise-Grade Security: Provides SOC II compliance, HIPAA support, and secure ingestion architecture.

Skip the operational overhead and focus your team on outcomes, not infrastructure. Download the guide to see why DataHub Cloud is the secure, scalable solution for your AI future.

In today's data-driven landscape, a robust data catalog is essential for turning raw information into actionable intelligence. However, the market has evolved rapidly, and most traditional catalogs can no longer meet modern demands around AI, real-time data, and fragmented governance.

This buyer's guide is designed to equip you with the precise questions needed to evaluate data catalog vendors thoroughly. Cut through marketing promises and technical jargon to ensure you select a solution that truly future-proofs your data infrastructure.

This comprehensive RFP toolkit covers 10 critical evaluation categories, including:

• Scalability: How fast is the metadata ingested? Does it support real-time updates from streaming and massive cloud sources?
• Extensibility: Can you easily extend the metadata model to capture unique business concepts and custom relationship types?
• AI-Ready: Does the platform natively catalog ML models, track lineage between training data and model outputs, and use AI to enhance documentation?
• Unification: Does the solution truly unify discovery, observability, and governance, or is it a collection of separate, disconnected modules?

Your choice of data catalog will fundamentally shape how your teams discover, understand, and leverage data for years to come. Download the guide and invest wisely in your data future.

More than a decade after its introduction, BCBS 239 remains one of the most critical frameworks shaping how financial institutions manage risk data. Yet, persistent challenges like fragmented IT and inconsistent data quality continue to hinder progress. Non-compliance carries severe consequences, but meeting the mandate offers a rare opportunity: the same controls that satisfy regulators also build a foundation for trustworthy AI adoption.

This guide from DataHub shows how financial institutions can move beyond checking the compliance box to building resilient, future-ready data foundations. It maps all 14 BCBS 239 principles directly to DataHub's metadata-native capabilities.

Inside, you will learn how to operationalize the mandate and achieve a strategic advantage:

• Governance & Infrastructure (Principles 1 & 2): Establish clear ownership and unified, consistent risk definitions across fragmented IT landscapes.
• Risk Data Aggregation (Principles 3-6): Ensure accuracy, completeness, and timeliness using end-to-end data lineage and real-time metadata.
• Risk Reporting (Principles 7-11): Deliver reports that are accurate, comprehensive, and auditable, even under market stress.
• Beyond Compliance: Turn BCBS 239 controls into launchpads for operational efficiency and AI/ML readiness.

Compliance doesn't have to be a cost center. Download the guide to gain the strategy and technology needed to build your risk data advantage.

Companies are investing billions into AI, yet more than 80% of AI projects fail to make it past the pilot phase. The problem isn't the technology, it's the data foundation.

AI systems require context (metadata, lineage, relationships) to make sense of the data they are fed. Without it, AI becomes expensive guesswork and a liability.

This practical guide from DataHub addresses the AI Gap No One Talks About by showing you how to build an intelligent metadata foundation that powers successful AI.

Inside this guide, you will explore the three critical layers of context needed for AI success:

• Technical Context: Understanding data provenance, schema, and version control.
• Operational Context: Tracking data behavior, reliability, and service-level agreements (SLAs).
• Business & Social Context: Providing the human layer, ownership, and compliance policies that ground AI in business intent.

DataHub Cloud provides the intelligent, event-driven context your data and AI need to perform, scale, and deliver results. Learn how to get your AI models into production faster and ensure compliance at scale.

Traditional data catalogs were simple inventories for basic SQL needs, but they can't handle the volume, variety, and velocity of today's decentralized data ecosystems. As your organization embraces digital transformation and AI, the limitations of older catalogs create fragmentation, governance chaos, and critical blind spots.

Modern metadata platforms are the solution. They function as dynamic, operational assets that integrate deeply with your production pipelines, enabling systems and AI models to orchestrate and manage data in real time.

This guide from DataHub explores seven compelling reasons why your organization needs to move beyond its traditional catalog and adopt a future-proof metadata platform.

Inside, you will learn the critical differences in:

• Scalability: Handling billions of data objects, not just basic inventories.
• Unification: Breaking down silos by combining discovery, observability, and governance in one platform.
• AI-Readiness: Natively cataloging ML models, feature stores, and training data while using AI to enhance metadata management.
• Future-Proofing: Designing a platform that evolves continuously to meet new standards and regulatory requirements.

The transition is not just a technology upgrade, it's a fundamental shift required to maximize value from your data assets and enable your AI strategy.

As Foursquare grew through multiple acquisitions, they inherited a sprawling and fragmented data ecosystem. This created a severe lack of standardization, leading to costly data duplication, inconsistent lineage, and a discovery process that slowed developer productivity and release cycles.

This success story reveals how Foursquare transformed their data stack by implementing DataHub's flexible, lineage-rich metadata control plane, enabling a developer-first data platform vision.

You will learn how Foursquare:

• Accelerated time-to-discovery and access from days to minutes, dramatically improving developer productivity.
• Adopted DataHub's flexible metadata model to integrate disparate systems (Amazon S3, Redshift, Databricks, and Airflow) without vendor lock-in.
• Automated metadata lineage and established fine-grained access control for sensitive geospatial data.
• Built a scalable control plane that improved visibility into upstream/downstream dependencies, enabling scalable cross-team data reuse.

Download the case study to see how the world's leading geospatial technology company mastered its data complexity.