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The AWS Developers Podcast

Amazon Web Services

Stay updated on the latest AWS news and insights for developers, wherever you are, whenever you want.

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  • 22 episodes
  • weekly
  • Avg 57 min
  • English

Episodes 22

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Counted on this page — what you have heard stays on this device, so it is not something the list can be paged by.
  • #201
    March 25 · 1 hr 13 min

    The Hard Lessons of Cloud Migration: inDrive's Path from Monolith to Microservices

    Join us for a fascinating conversation with Alexander 'Sasha' Lisachenko (Software Architect) and Artem Gab (Senior Engineering Manager) from inDrive, one of the global leaders in mobility operating in 48 countries and processing over 8 million rides per day. Sasha and Artem take us through their four-year transformation journey from a monolithic bare-metal setup in a single data center to a fully cloud-native microservices architecture on AWS. They share the hard-earned lessons from their migration, including critical challenges with Redis cluster architecture, the discovery of single-threaded CPU bottlenecks, and how they solved hot key problems using Uber's H3 hexagon-based geospatial indexing. We dive deep into their migration from Redis to Valkey on ElastiCache, achieving 15-20% cost optimization and improved memory efficiency, and their innovative approach to auto-scaling ElastiCache clusters across multiple dimensions. Along the way, they reveal how TLS termination on master nodes created unexpected bottlenecks, how connection storms can cascade when Redis slows down, and why engine CPU utilization is the one metric you should never ignore. This is a story of resilience, technical problem-solving, and the reality of large-scale cloud transformations — complete with rollbacks, late-night incidents, and the eventual triumph of a fully elastic, geo-distributed platform serving riders and drivers across the globe. With Alexander Lisachenko, Software Architect, inDrive ; With Artem Gab, Senior Engineering Manager, Runtime Systems, inDrive Redis in Action — Josiah L. Carlson (Manning) AWS Well-Architected Framework — ElastiCache Lens Brendan Gregg's Blog — Performance Analysis & Observability Uber H3 — Hexagonal Hierarchical Spatial Index inDrive Website AWS ElastiCache Documentation Valkey Project AWS Well-Architected Framework

  • #200
    March 18 · 51 min

    Spring AI and AgentCore: Building Enterprise AI Agents in Java

    It's a milestone — episode 200! And to mark the occasion, we're doing something we've never done before: hosting two guests at the same time. James Ward (Principal Developer Advocate at AWS) and Josh Long (Spring Developer Advocate at Broadcom, Java Champion, and host of 'A Bootiful Podcast') join Romain for a wide-ranging conversation about why Java and Spring AI are becoming the go-to stack for enterprise AI development. We kick off with Spring AI's rapid evolution — from its 1.0 GA release to the just-released 2.0.0-M3 milestone — and why it's far more than an LLM wrapper. James and Josh break down how Spring AI provides clean abstractions across 20+ models and vector stores, with type-safe, compile-time validation that prevents the kind of string-typo failures that plague dynamically typed AI code in production. The numbers back it up: an Azul study found that 62% of surveyed companies are building AI solutions on Java and the JVM. James and Josh explain why — enterprise teams need security, observability, and scalability baked in, not bolted on. We dive into the Agent Skills open standard from Anthropic and James's SkillsJars project for packaging and distributing agent skills via Maven Central. We also cover Spring AI's official Java MCP SDK (now at 1.0) and how MCP and Agent Skills complement each other for building capable, composable agents. The performance story is striking: Java MCP SDK benchmarks show 0.835ms latency versus Python's 26.45ms, 1.5M+ requests per second versus 280K, and 28% CPU utilization versus 94% — with even better numbers using GraalVM native images. Josh and James also walk us through Embabel, the new JVM-based agentic framework from Spring creator Rod Johnson, featuring goal-oriented and utility-based planners with type-safe workflow definitions built on Spring AI foundations. We close with a look at running Spring AI agents on AWS Bedrock AgentCore — memory, browser support, code interpreter, and serverless containers for agentic workloads. With James Ward, Principal Developer Advocate, AWS ; With Josh Long, Spring Developer Advocate, Broadcom — Java Champion Spring AI Documentation Start building with Spring — start.spring.io Spring AI 2.0.0-M3 Release Announcement Embabel — Agentic framework for the JVM by Rod Johnson SkillsJars — Agent Skills via Maven Central Agent Skills Open Standard (Anthropic) Amazon Bedrock AgentCore Coffee + Software — Josh Long's YouTube channel A Bootiful Podcast — Josh Long James Ward's blog and presentations Josh Long's website DevNexus 2026 (Atlanta, March 4–6) Voxxed Days Zurich 2026 (March 24)

Showing 21–22 of 22 episodes