Operations | Monitoring | ITSM | DevOps | Cloud

Having a slow PostgreSQL or MySQL? Check out how you can get a holistic view of your database workloads and AI-driven optimization suggestions for indexes and SQL rewrites.

In the world of software development, caching is a crucial yet complex component. What happens when one of the most popular caching systems, Redis, changes its license, sending shockwaves through the community? And how does a company pivot to not one but two solutions to ensure high-performance data management? Today's discussion dives deep into one of the most critical and often-overlooked components of the software industry—caching.

Imagine a world where databases not only store vast amounts of data but autonomously optimize themselves, predict future trends, and seamlessly integrate with AI. In this Data (R)evolution episode, we share a fascinating conversation with Soumya Bijjal, the VP of Product at Aiven, and John Joseph Kennedy, the Head of Databases at Aiven, about the future of AI, databases, and data platforms. They dissect the current AI landscape, the challenges companies face, and the transformative potential of integrating AI with data systems.

Aiven AI Database Optimizer: Troubleshoot PostgreSQL and MySQL database performance issues within seconds, get instant optimization recommendations, and reduce your databases cost. Powered by EverSQL by Aiven.

In this session, we will show how to leverage Aiven for Dragonfly and Aiven for AI. First, we’ll discuss how to increase your throughput and reduce memory usage by 25% compared to open-source Redis. Then explore scalability, efficiency, and advanced capabilities ideal for caching, gaming leaderboards, messaging, AI applications, and more. After that, we’ll jump into Aiven’s latest AI use cases and cover.

In this workshop we’ll work together to generate embeddings for podcast transcriptions and load that data into OpenSearch. Then we’ll search the documents using similarity search and use those results to improve our responses from an LLM (Large Language Model). Along the way we’ll explain the Retrieval Augmented Generation (RAG) pattern and show how it’s possible to try different LLMs without having to completely rewrite your code.