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StarRocks 4.0: Bringing Native Columnar Performance to JSON

JSON was never designed for analytics. It’s extremely flexible, universal, and perfect for fast-changing data—but once those JSON logs ...

StarRocks 4.0: Delivering Query-Ready Data to Apache Iceberg

Data in your Apache Iceberg tables often doesn't land in a way that's optimized for queries. Continuous micro-batch writes create tens ...

Announcing StarRocks 4.0: Open, Fast, Governed

StarRocks 4.0 is here! Unveiled at the StarRocks Global Summit, version 4.0 marks the next major milestone in the StarRocks journey. In...

Data Skew in Customer-Facing Analytics: The Hidden Cost Behind Latency

In customer-facing analytics, delivering fast, interactive insights isn’t just a bonus—it’s a baseline expectation. Users expect sub-se...

Why Customer-Facing Analytics Needs More Than Just Fast

Delivering analytics to customers might seem like an extension of what internal BI platforms already do—running SQL queries, rendering ...

Customer-Facing Analytics Meets Agents: Eightfold‘s Move from Redshift to StarRocks

Eightfold.ai needed sub-second, multi-tenant analytics—today for humans, tomorrow for AI agents. By migrating from Redshift to StarRock...

Customer-Facing Analytics Without Denormalizing Everything

For years, denormalization has been the go-to workaround when query engines couldn’t handle complex joins at scale. Flatten your data, ...

3.5 Webinar Q&A Recap: StarRocks Security, Iceberg Support, and More

Thanks to everyone who joined our StarRocks 3.5 webinar! We received great questions from the audience—here’s a quick summary of some o...

Introducing StarRocks 3.5

StarRocks 3.5 introduces a focused set of improvements across reliability, performance, and security. This release adds cluster-level s...