Anton Aleksandrov

Agents running generated code, multi-tenant platforms, and untrusted workloads all need strong isolation, but each traditional compute option forces a tradeoff. VMs isolate well but start slowly.

Building Kafka consumers today means running, scaling, and patching compute infrastructure yourself, and writing your own retry, poison-pill, and observability plumbing. AWS Lambda's event source mapping for Apache Kafka removes that operational burden, letting you process streams from AWS MSK, self-managed Kafka, and Confluent Cloud with nothing more than function code. In this session, see how to build low-latency, high-throughput consumers that scale automatically with partition lag, decode and validate records against Confluent Schema Registry, and cut cost by filtering JSON, Avro, and Protobuf events before invocation. The same event-driven pattern can trigger AI agents directly from your streams, so events can kick off agentic workflows without extra glue code. We cover tuning for throughput and latency, extensive CloudWatch metrics and logs for consumer visibility, and error handling patterns including retries, bisect-on-error, and on-failure destinations for dead-letter queues. By attending this session, your contact information may be shared with the sponsor for relevant follow up for this event only.

In this session, learn about AWS Lambda's execution environment lifecycle, diving deep into how the service manages isolation at the function level, and understanding the security implications of environment reuse patterns.

Agentic AI is transforming enterprise applications, but taking it from prototype to production requires more than just prompting. Join this chalk talk to discuss pragmatic, scalable patterns for building agentic AI systems using AWS Serverless services like Lambda, ECS Fargate, and API Gateway.

Many development teams rely on Apache Kafka for streaming data across a variety of business-critical applications. AWS Lambda integrates natively with Apache Kafka, offering a serverless approach to event-driven data processing.

As serverless adoption grows, teams need consistent patterns for security, observability, governance, and CI/CD to avoid duplicating efforts and architectural inconsistencies.This session explores serverless platform engineering through a lens of architectural blueprints: a structured approach that promotes…

AWS Lambda event source mappings simplify building the processing layer for streaming workloads. Connect your event source to your Lambda function to use built-in integrations to filter and batch messages and readily handle errors.

In this session, discover best practices and proven patterns for using Terraform to build serverless applications safely, predictably, and repeatedly. Learn techniques for designing modular, reusable architectures and strategies to test applications locally.

In this chalk talk, learn how to optimize your streaming data processing with AWS Lambda. Explore scenarios where default processing speeds may bottleneck workloads consuming messages from Apache Kafka, Amazon DynamoDB, or other sources—especially when data enrichment is required.

In this chalk talk, discuss customer stories, best practices, and architectures for building SaaS solutions with serverless functions and container technologies. Dive deep into common challenges and how to sidestep them with a serverless-first mindset.

Building distributed solutions with functions and microservices often requires coordinating workflows. You may need a data processing workflow that coordinates multiple services like AWS Lambda, Amazon S3, and Amazon Athena.

In this chalk talk, consider and discuss customer stories, best practices, and architectures for building SaaS solutions with serverless and container technologies. Dive deep into common challenges and how to sidestep them with a serverless-first mindset.

Building distributed solutions with functions and microservices requires orchestrating workflows. You might need a workflow for engineers to request cloud resources and managers to approve requests.