Aaron talks with Ovais Tariq, co-founder and CEO of Tigris Data and former Uber engineer who helped scale one of the world’s largest distributed systems. They discuss Uber’s hyperscale infrastructure, what it takes to build an S3-compatible object store from scratch, and how distributed storage is evolving for the AI era.
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Chapters: 00:00 - Introduction and overview of the episode 01:35 - Ovais’s background and introduction to Tigris 03:00 - Building distributed databases and infrastructure at Uber 06:00 - Uber’s in-house philosophy and massive data scale 09:00 - Hardware, power density, and talking to chip manufacturers 12:00 - Learning curve of scaling hardware and data centers 14:00 - The Halloween outage and lessons from Cassandra 16:00 - Building data centers across the world for Uber 17:00 - Founding Tigris and the vision for global storage 18:45 - How Tigris differs from AWS S3 20:00 - The architecture of Tigris: caching, metadata, and replication 32:00 - Why Tigris uses FoundationDB and its reliability 36:00 - Managing global and regional metadata 38:00 - How Tigris dynamically moves and caches data 41:30 - Building their own data centers and backbone 43:45 - Specialized storage for AI workloads 46:00 - Small file optimization and real-world use cases 49:00 - Snapshots, forking, and agentic AI workflows 51:00 - How AI transformed Tigris’s customer base 54:00 - Partnership with Fly.io and the distributed cloud ecosystem 57:00 - Growth, customers, and focus on media and AI companies 59:00 - What’s next for Tigris: distributed file system plans 1:01:00 - Technical challenges and building trust in durability 1:03:00 - Call to action: try Tigris and upcoming snapshot feature 1:05:00 - Advice for engineers leaving big companies to start something new 1:06:30 - Where to find Ovais online and closing remarks
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