Amagi is a global media technology company that enables streaming platforms to scale channel delivery and empowers advertisers with data-driven insights to target audiences. Supporting 8,000+ channels, 300+ distributors and 26 billion annual ad impressions for 45% of the world’s top media companies, Amagi relies on real-time data for ad decisioning, content performance analytics, billing and customer reporting. As streaming volumes and global operations grew, fragmented data systems and complex multicloud environments limited speed, consistency and governance. By leveraging the Databricks Platform, Amagi created a single, governed source of truth that supports both internal teams and customer-facing products—cutting costs by 45% while accelerating time to market and improving reliability at scale.
Legacy data platforms needed to evolve to Agentic and AI ecosystem
Amagi operates at the intersection of three critical stakeholder groups: content owners seeking frictionless distribution, video service providers requiring limitless content access, and advertisers pursuing targeted reach. This three-sided marketplace demanded unprecedented levels of data integration, real-time processing, and intelligent decision-making capabilities.
For years, Amagi’s legacy data infrastructure served the company well. However, as the business scaled globally and customer demands evolved, fundamental cracks began to appear. The organization faced four interconnected challenges that threatened to limit future growth.
Data fragmentation created silos across the organization. Multiple teams maintained separate data systems — Dataproc for batch processing, Snowflake for analytics, and various in-house platforms for specialized workloads. Each system had its own governance model, access controls, and data definitions. Finance teams saw different numbers than Operations, while Product teams struggled to get consistent answers about customer behavior. This fragmentation made it nearly impossible to establish a single source of truth.
Cross-region data governance became increasingly complex. Operating across multiple cloud environments (AWS and GCP) in different geographic regions, Amagi struggled to enforce consistent data access controls and compliance requirements across these environments. The lack of unified governance meant teams spent excessive time on data reconciliation rather than innovation.
“We had various use cases emerging around both batch and agentic workloads,” explained Ravi Teja Chilukuri, Director of Data Platform at Amagi.
”The need for data insights wasn’t limited to serving insights to our customers. We needed a single source of truth for operations, product and finance teams. No single legacy infrastructure could efficiently serve all these constituencies simultaneously.”
Building the Amagi Data Platform on Databricks
Rather than incrementally patching legacy systems, Amagi made a strategic decision to architect a unified data platform from the ground up, choosing Databricks as the foundational technology. The resulting Amagi Data Platform (ADP) represents a complete reimagining of how data flows, transforms, and delivers value across the organization.
A unified lakehouse architecture eliminates data silos. The ADP is built on a modern lakehouse that elegantly handles Amagi’s full spectrum of data needs. At the foundation lies a comprehensive data ingestion and validation framework that processes data from all source systems: content metadata, asset catalogs, advertising data, service streams, program schedules, and user data. Rather than creating isolated silos, these diverse sources flow into a governed data lake via Unity Catalog.
Unity Catalog enables Amagi to define data lineage, enforce access controls, tag sensitive data, and ensure compliance across all data stores — capabilities that were simply not possible in the fragmented legacy environment. For the first time, teams could trace data from source to insight, understanding exactly where each metric originated and how it transformed along the way.
One platform serves multiple consumers. What makes the ADP truly revolutionary is that it serves not just one use case, but multiple consumers from a single, governed source of truth. Internal stakeholders access data through Databricks notebooks, Genie for natural language querying, SQL warehouses, and serverless compute. Finance teams get real-time billing and financial reporting. Operations teams monitor KPIs and platform health. Product teams analyze customer usage patterns.
This unified access means that when Marketing inquires about customer behavior, their data aligns with Finance’s revenue data, which in turn aligns with Operations’ infrastructure metrics. The days of different teams presenting conflicting numbers in executive meetings are over.
Customer-facing data products deliver differentiated value. Streaming platforms receive advanced reporting and analytics on content performance. Ad tech partners access ad intelligence and personalized attribution. Content owners get detailed usage reports and revenue breakdowns. All external deliverables ensure consistency and eliminate reconciliation nightmares.
“This wasn’t just a technical upgrade. It was a fundamental shift in how the organization thinks about data reliability,” stated Ravi. “We moved from asking ‘Where should we look for this data?’ to ‘What insights do we need?’ The platform handles the complexity.”
Data becomes a competitive advantage
By consolidating fragmented systems like Dataproc, Snowflake and multiple in-house platforms into the unified Amagi Data Platform on Databricks, including their multicloud setup on AWS and GCP, Amagi unlocked significant operational and financial benefits.
Dramatic cost reduction without sacrificing capability. The platform delivered 45% cost savings by eliminating redundant infrastructure and optimizing compute usage. Rather than running multiple systems 24/7, Amagi now leverages Databricks’ serverless compute to scale resources dynamically based on actual demand. These savings came without any reduction in capability. In fact, teams gained access to more powerful analytics and AI capabilities than ever before.
Accelerated innovation cycles. The time to market for new data products decreased by 15x. What previously took months of infrastructure planning and data pipeline development now happens in days. Data engineers can rapidly prototype new use cases, validate them with real data, and deploy to production without waiting for infrastructure provisioning or cross-team coordination.
Improved operational reliability. Production incidents decreased by 35% thanks to better data quality, automated validation, and comprehensive lineage tracking. When issues do occur, teams can quickly identify their source and implement fixes. The platform’s built-in observability enables problems to be detected and resolved before they impact customers.
Democratized data access across the organization. Perhaps most importantly, data is no longer a constraint but a competitive advantage. Insights that were once difficult and expensive to generate are now accessible to anyone who asks. New ideas can be validated in days rather than months. Teams make decisions based on data rather than intuition, knowing they’re all working from the same source of truth.
As the video economy continues to evolve, with content flowing across an ever-expanding array of platforms and audiences demanding increasingly personalized experiences, companies like Amagi that have invested in modern, unified, AI-ready data infrastructure will pull further ahead.
“In an industry where data drives every decision, from content creation to ad placement to customer retention,” Ravi concluded, “having the right data platform isn’t a nice-to-have, it’s the foundation of competitive advantage.”