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.
Discover how to build Agentic AI workflows using tools like n8n, YugabyteDB, and LangChain. By seamlessly integrating YugabyteDB’s vector embedding capabilities, OpenAI models, and n8n workflow orchestration, businesses can develop robust and scalable AI solutions that deliver real value.
The YugabyteDB MCP Server is a new, lightweight, Python-based server that allows LLMs like Anthropic’s Claude to interact directly with your YugabyteDB database. In this blog we demonstrate how MCP allows an AI application to access, query, analyze, and interpret data in your YugabyteDB database, using only natural language prompts.
As vector search becomes foundational to modern AI workloads, databases must rethink how their architecture handles high-dimensional vector data at scale. This blog reveals how YugabyteDB integrates a distributed vector indexing engine powered by USearch to deliver a fast, scalable, and resilient vector search natively with a Postgres-compatible SQL interface.
Non-resilient applications can have a significant impact on business, including revenue loss, reputational damage, and decreased customer satisfaction. This blog introduces the concept of ultra-resilience, explains why it’s crucial for modern applications, and shares how YugabyteDB helps organizations achieve it.
As financial services evolve, there’s increasing demand for intelligent, real-time, personalized customer interactions. Discover how YugabyteDB, OpenAI, and Model Context Protocol (MCP) can be used together to build a modern, scalable, and intelligent chatbot for financial insights.
YugabyteDB now offers a MongoDB-compatible API with the support of the DocumentDB PostgreSQL extension, providing developers with an open source alternative for MongoDB workloads that need to scale horizontally. Follow this step-by-step guide to deploying and running MongoDB workloads on YugabyteDB in collaboration with Microsoft (DocumentDB) and FerretDB.
Implementing CDC using Spring Boot, Debezium’s embedded engine, and YugabyteDB offers a compelling strategy for real-time data synchronization without the complexities of a dedicated middleware. Discover why this embedded approach is ideal for scenarios that require simplicity, low latency, and reduced operational complexity.
The growth of online sports betting brings new opportunities, but also highlights technical limitations in many platform architectures—especially at the database layer. Discover the key challenges sportsbook providers face today and explore how modern database infrastructure can provide a critical competitive edge.
RAG architectures hold the key to moving beyond static, pre-trained models by anchoring responses in live, curated data. In this blog, we explore how YugabyteDB, can power the retrieval layer of a RAG pipeline—offering scale, resilience, and low-latency access to semantically rich data. Whether you’re building smart chatbots, enterprise search, or generative AI assistants, this setup can improve performance, operational efficiency, and cost-effective scaling.
