Generative AI is transforming how we build and operate data‑driven applications. Whether you are a developer eager to embed semantic search into your app, a business leader exploring AI‑powered analytics, or a DBA looking to automate routine tasks, the right database foundation is critical. This new guide will help you choose the best GenAI option for your database needs.
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.
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.
Peak events like Black Friday sales, record-breaking concerts, and unpredictable freak events like adverse weather and cloud outages, are pushing legacy database technology’s scalability and resilience limits.
With even more frequent and extreme peak events and freak events likely in the years to come, discover how distributed SQL can help!
We just hit the impressive milestone of 10,000 YugabyteDB open source community members – thanks to everyone who has been part of this remarkable journey! Find out how you can join our thriving community, and discover more resources to help you make the most of YugabyteDB.
Having different names for our YugabyteDB DBaaS deployment options has led to some confusion. To make it clear that these are two deployment options of one core product we’ve unified the product name – YugabyteDB Aeon! In this blog, we share the benefits of YugabyteDB Aeon and detail how to choose the best deployment option for your use case.
Developers can now store, index and query vectors in PostgreSQL using pgvector, eliminating the need for a dedicated vector database. This blog deep dives into pgvector, and explains how to store text embeddings as vectors, execute similarity search, choose between indexes, and scale with distributed SQL.
Discover efficient methods to partition data in your queries. Explore techniques like GROUP BY, window functions, recursive CTE queries, and the DISTINCT ON clause to optimize performance and scalability
Discover how the LIKE operator enables pattern matching with wildcards and indexing for specific access patterns, focusing on scenarios where the wildcard is at the beginning or end of the pattern. The examples and demonstrations are performed on YugabyteDB, a PostgreSQL-compatible database with variations in storage implementation.
When indexing JSON documents in PostgreSQL, you can add indexes for the access patterns just like with relational table columns. We will walk through an example of this using the Pokémon GO Pokédex as a dataset, utilizing YugabyteDB, a distributed SQL database compatible with PostgreSQL.
