This blog demonstrates how common distributed SQL system patterns can create write hotspots in distributed architectures, and how ordering, key design, and access paths directly impact scalability. Understanding and addressing the trade-offs is now significantly simpler with YugabyteDB, which enables true lift-and-shift of high-write-throughput applications.
What happens when a relational database design built for e-commerce is pushed to planetary scale? For Shopify, the answer involved years of custom sharding, massive replication overhead, and growing application complexity. Discover how Shopify is navigating the move from large-scale MySQL deployments to YugabyteDB, and what that shift means for engineers building systems at extreme scale.
Discover how to turn scattered documents into a connected knowledge graph using Dify and YugabyteDB. Dify handles AI orchestration while YugabyteDB serves as the unified data backbone, consolidating vectors, relationships, and metadata in one operationally straightforward platform. The end result is a robust system that’s simpler to operate, cost-efficient to scale, and easier to take from prototype to production-ready platform.
YugabyteDB Anywhere’s Dynatrace export configuration is a clean, centralized way to stream universe metrics into Dynatrace. This blog provides a detailed, step-by-step guide to using the YugabyteDB Anywhere metrics export configuration (OTLP/HTTP) with Dynatrace (Observability Platform).
When planning the next step for your organization’s data infrastructure, the first decision is whether to migrate your existing data “as‑is” or modernize your data platforms. This blog explains the difference between data modernization and data migration, when to choose each approach, and why adopting a distributed database is often central to a successful modernization strategy.
As performance infrastructure expands, so does the volume of data generated. This can make the review process challenging. Discover how the YugabyteDB performance team addressed this issue by building two AI-driven systems, one focused on regression detection and the other on making performance knowledge easier to query and share.
Read this blog (and the related book) to gain a deep, practical understanding of vector databases, their significance in the AI era, and when they are not applicable. Learn why YugabyteDB is an ideal choice for Vector search and AI workloads, and how vector stores, complete vector databases, and hybrid systems compare, plus how you can model, index, query, scale, and operate vector and relational workloads together.
Thanks to this new integration, you can turn Visual Studio (VS) Code into a fully-fledged MCP client for your YugabyteDB database. This blog guides you through using VS Code as the MCP client and connecting it to the YugabyteDB MCP Server. It also details how you can explore the YugabyteDB database via natural-language prompts.
AI doesn’t change the importance of data, it simply exposes weaknesses faster and at a much larger scale. If you’re building or modernizing AI-driven systems, the database you choose matters more than ever. Download our new white paper to discover what to look for in a next-generation data platform and how YugabyteDB meets those demands.
YugabyteDB is officially supported in Google’s MCP Toolbox for Databases, making it easier for you to build production AI agents with confidence. Discover how developers can access YugabyteDB’s distributed SQL capabilities, including load balancing, topology-aware routing, and data sovereignty controls, as documented and tested configuration options rather than custom workarounds.
