Vlad Vlasceanu

Payment dispute resolution and ledger reconciliation require agents that reason over transactional history, retrieve precedent with precision, and write through idempotent workflows.

In this session, learn how Netflix successfully orchestrated the migration of terabytes of mission-critical data across 100+ clusters to Amazon Aurora while ensuring continuous service for millions of global subscribers.

Multi-agent collaboration unlocks new levels of reasoning, specialization, and task orchestration in generative AI systems. In this hands-on workshop, learn how to harness Model Context Protocol (MCP) with AWS agentic AI services and Amazon Aurora PostgreSQL-Compatible Edition to build a collaborative AI system.

Do you want to build an MCP server for your application running on Amazon Aurora where you control the access without handing full control to an LLM and the AI agents simply call it?

PostgreSQL performance isn’t magic—it’s engineering. In this code talk, we dive deep into practical tuning techniques to avoid common pitfalls that silently degrade performance by improving an underperforming PostgreSQL database workload.

Does the thought of modernizing your application and database architecture fill you with concern? In this code talk, learn how to overcome common migration issues and concerns that arise during modernization projects.

Do you want to build a generative AI application with Amazon Aurora but are unsure of how to get started? In this code talk, learn essential tools for creating your own generative AI applications, including LangChain and pgvector.

Chaos engineering is the discipline of experimenting on a system to build confidence in the system’s capability to withstand abnormal conditions in production. Recreating real-world failure conditions helps uncover hidden bugs as well as monitoring omissions and performance bottlenecks.

In this chalk talk, learn how to evaluate the right database for your application. Often when organizations are moving from monolithic databases they have to evaluate a daunting number of factors to determine the best option for their workloads.

Modern applications are increasingly global, serving a user base dispersed around the world, and users expect the best possible experience no matter where they are located. In this chalk talk, discover how AWS global database service capabilities can help you scale your workloads to serve a global audience.

Amazon Aurora is a MySQL- and PostgreSQL-compatible relational database built for the cloud that combines the performance and availability of traditional enterprise databases with the simplicity and cost-effectiveness of open-source databases. In this session, hear about some of the unique as well as common features of Aurora MySQL and PostgreSQL and learn about the design considerations for...

For relational-database workloads, customers are increasingly adopting Amazon Aurora to take advantage of Aurora's performance, high availability, durability, manageability, and innovations. The proof of concept (PoC) is the main process and decision-making step to evaluate and adopt Aurora.

Learn how Amazon Relational Database Services (RDS) helps SQL Server DBAs focus their time where it matters most. We will cover Amazon RDS for SQL Server features and the key differences between running deployments on Amazon Elastic Cloud Compute (EC2) and Amazon RDS.

Amazon Aurora is a fully managed MySQL and PostgreSQL-compatible relational database with the speed, reliability, and availability of commercial databases at one-tenth the cost. It is up to five times faster than standard MySQL databases and three times faster than standard PostgreSQL databases.

Amazon Web Services (AWS) offers the ability to run SQL Server both as a managed service part of the Amazon Relational Database Service (Amazon RDS), as well as letting you deploy your own custom solution on Amazon EC2.