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.