Nilanjan Chatterjee

As Databricks workloads scale, the efficiency of the underlying CPU platform directly impacts performance, cost, and infrastructure utilization. In this session, we introduce AMD’s latest CPU platform, Turin, and show how it compares to prior generations in both performance and efficiency across analytics workloads. AMD engineers will then demonstrate how their internal GPU telemetry team runs large‑scale analytics workloads on Databricks—and why those workloads perform more efficiently on AMD CPUs. Using a production telemetry pipeline processing minute‑level signals across thousands of nodes, we highlight measurable gains in throughput, efficiency, and reduced infrastructure requirements for the same job. Attendees will learn how to replicate these results and explore a new AMD–Databricks pilot program to validate performance improvements through guided proof‑of‑concepts.

SQL indexes are a powerful tool for optimizing database performance, but their effectiveness depends on platform-specific strengths and trade-offs. In Azure SQL, automated features like index tuning and columnstore indexes streamline analytical workloads, while managed maintenance reduces fragmentation risks.