Speaker

Venu Thangalapally

Venu Thangalapally

Financial Services - Sr Solutions Architect , Amazon Web Services

Chicago, Illinois, United States

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Senior Solutions Architect at AWS, based in Chicago, with deep expertise in cloud architecture, data and analytics, containers, and application modernization. He partners with financial service industry customers to translate business goals into secure, scalable, and compliant cloud solutions that deliver measurable value. Venu is passionate about using technology to drive innovation and operational excellence.

Area of Expertise

  • Finance & Banking

Topics

  • Data Platform
  • Data Engineering
  • Data Analytics
  • Data Science & AI
  • Big Data
  • Data Visualization
  • Analytics and Big Data
  • Data Management
  • All things data
  • Generative AI
  • Retrieval Augmented Generation
  • Enterprise Patterns in Generative AI

Multi-Engine Iceberg: Running Spark, Athena, and Redshift on the Same Tables

One of Iceberg's promises is engine independence, but making it work in production requires careful planning. This session shares battle-tested patterns for running multiple query engines against the same Iceberg tables.
Topics include: managing concurrent writes across engines, optimizing file layouts for different query patterns, handling schema evolution across Spark/Athena/Redshift, and monitoring cross-engine performance. Includes a live demo showing the same dataset queried by three engines simultaneously.

Iceberg Performance Playbook: 8 Optimization Techniques with Benchmarks

Iceberg tables running slow? This comprehensive session reveals 8 proven optimization techniques with real benchmark data from production AWS workloads. Discover
how to achieve dramatic performance improvements through: (1) Distribution modes for optimal file layout, (2) Statistics optimization for query pruning, (3) Z-
ordering for multi-dimensional sorting, (4) Compaction strategies to eliminate small files, (5) File size optimization for metadata efficiency, (6) Partition
evolution without data rewrites, (7) Spark Adaptive Query Execution integration, and (8) Puffin files for advanced statistics. Real-world example: 10 billion row,
5TB table optimized from 312 seconds to 18 seconds—a 17x speedup with 93% cost reduction. Each technique includes specific benchmarks, and practical guidance on when to apply them. Learn the optimization decision tree and avoid common pitfalls.

Iceberg Metadata - Understanding Hidden Costs at Scale

Your Iceberg table works great, until you realize metadata operations are consuming more resources than your actual queries. This lightning talk reveals the hidden costs of metadata management at scale: snapshot accumulation, manifest file proliferation, and metadata query patterns that kill performance. Learn quick wins for metadata hygiene, optimal snapshot retention policies, and when to use metadata tables vs. direct file inspection.

Iceberg in the Regulated World: Compliance, Auditing, and Data Governance

Financial services and healthcare organizations face strict data governance requirements. This session explores how Iceberg's immutable metadata and time travel capabilities enable compliance use cases: audit trails, data lineage tracking, point-in-time recovery for regulatory reporting, and GDPR right-to-be-forgotten implementations. Learn how to leverage Iceberg's metadata tables for compliance automation and build governance frameworks that auditors love.

Venu Thangalapally

Financial Services - Sr Solutions Architect , Amazon Web Services

Chicago, Illinois, United States

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