Distributed SQL is a class of cloud-native, highly scalable, and resilient databases designed for today’s always-on, data-driven world. Download Distributed SQL Databases For Dummies (2nd Yugabyte Special Edition) today and explore how distributed SQL can help you achieve ultra-resilience, simplify operations, accelerate innovation, and future-proof your next GenAI applications.
Yugabyte has been recognized as a Sample Vendor in the Gartner® Hype Cycle for Data Management, 2025, and Hype Cycle for Cloud Computing, 2025. This recognition further validates YugabyteDB’s position at the forefront of distributed transactional databases and intercloud data management markets. Discover how ultra-resilient YugabyteDB allows organizations to run mission-critical applications with confidence across distributed environments.
YugabyteDB Aeon recently introduced Performance Advisor, a powerful new tool that brings intelligent observability directly into your distributed Postgres-compatible database. From real-time load charts to anomaly detection and query-level diagnostics, Performance Advisor allows tech teams to solve performance issues faster and spend more time building their applications. Read this blog to discover the latest YugabyteDB Performance Advisor features.
As an open-source, distributed SQL database built on top of PostgreSQL, YugabyteDB is committed to maintaining deep compatibility with the PostgreSQL ecosystem. In this blog, we share our approach to aligning YugabyteDB with the latest PostgreSQL versions, the challenges we face, and our future plans to narrow the compatibility gap.
Modern organizations use a variety of databases to manage information. As AI and machine learning models continue to grow in popularity, a new category has emerged: vector databases. This blog provides a clear definition of vector databases and answers key questions about how they work, how they differ from regular databases, and when you should use them for your AI and ML projects.
In this post, we walk through how to build a RAG pipeline using YugabyteDB’s new vector capabilities to enable smarter and more context-aware support automation. Discover how to ingest internal documents, vectorize them, store them efficiently in YugabyteDB, and finally how to use an LLM like GPT-4 to answer internal questions—rooted in your own support stack data.
The AI app-building landscape is evolving rapidly – you’ll soon be able to quickly build robust AI applications that scale from thousands to billions of vectors using familiar PostgreSQL and powerful vector search capabilities, without architectural changes. In this blog, we examine the current and future AI landscape and share the benefits of the new and enhanced AI functionality introduced in the latest YugabyteDB release.
Imagine a database that allows you to recover that critical row you deleted in production, and lets you test schema changes on real data without risking the original. Discover how YugabyteDB’s point-in-time capabilities, including point-in-time recovery, instant database cloning, and time travel queries, make it easy to rewind your data, spin up lightweight clones, and explore historical states with precision.
The YugabyteDB MCP Server brings your AI stack and your operational data into the same conversation. Easily accessible on AWS Marketplace, teams can now skip time-consuming procurement processes and deploy the YugabyteDB MCP Server in just a few minutes. Find out more.
Traditional architectures, particularly those using single-node or application-level sharding, are prone to failure and offer limited availability. In this blog, we consider two architecture options for scaling a database horizontally and employ probability theory to assess the resilience of each architecture to potential failure.
