StarRocks is an open‑source project under the Linux Foundation, focused on building a high‑performance, scalable analytical database that enables enterprises to create an efficient, unified lake‑house paradigm. It is widely used across many industries worldwide, helping numerous companies enhance their data analytics capabilities.
Ctrip's User Behavior Tracking (UBT) system, handling 30 TB of daily data, moved from ClickHouse to StarRocks' compute‑storage separated architecture, cutting average query latency from 1.4 seconds to 203 ms, halving storage, reducing nodes from 50 to 40, and boosting write throughput to 3 million rows per second.
Zepto transformed its brand‑analytics platform from a Postgres MVP into a production‑grade, sub‑second real‑time analytics solution by adopting StarRocks, redesigning its data pipeline with Databricks, Kafka, and Flink, and choosing a storage‑compute architecture that supports massive joins and rapid insights.
Facing massive data volumes, complex component dependencies, high TCO, and real‑time processing limits, the政采云 platform replaced its Hadoop stack with StarRocks’ minimalist, decoupled architecture, achieving lower costs, elastic scaling, faster queries, easier operations, and robust fault tolerance across diverse government procurement workloads.
Faced with billions of marketing events and minute‑level decision requirements during Taobao's flash‑sale campaign, the e‑commerce data team built a real‑time lakehouse using StarRocks and Paimon, leveraged asynchronous materialized views and RoaringBitmap deduplication, and achieved sub‑second query latency, massive cost savings, and stable high‑concurrency performance.
Facing daily data growth from millions to 150 billion records, Joydata‑U transformed its analytics platform through three architectural stages—Hadoop, Hadoop + Trino, and finally StarRocks—introducing resource isolation, Flat JSON acceleration, and Bitmap indexing to cut query latency by up to seven times and achieve sub‑2‑minute data freshness across BI, ad‑tech, game analytics, and CRM workloads.
This article details Qunar's transition to StarRocks as a unified OLAP engine, covering the business background, engine evaluation, architecture redesign, observability, high‑availability strategies, query‑performance optimizations, real‑world application cases, community contributions, and future plans.
This article explores the evolution of business intelligence toward intelligent BI, detailing traditional BI limitations, agile BI improvements, and how StarRocks' MPP lakehouse engine combined with large language models enables natural‑language analytics, real‑time performance, AI‑driven insights, and scalable enterprise deployments.
This step‑by‑step tutorial shows how to deploy StarRocks and MinIO in a decoupled storage‑compute architecture using Docker Compose and Kubernetes, configure local caching, create storage volumes, load public datasets, and run SQL queries to explore the combined data.
Shopee combined the Paimon data lake with StarRocks and Flink to create a quasi‑real‑time warehouse, enabling fast task diagnostics and a high‑performance financial reconciliation system while dramatically reducing storage costs and latency through innovative ODS, snapshot, and branch table techniques.
Suixingfu rebuilt its payment data pipeline by replacing a fragmented Lambda stack with a unified Porter CDC + StarRocks + Elasticsearch architecture, achieving three‑fold query speed, ten‑fold analytics efficiency, 20% storage reduction, and sub‑second data‑capture latency across high‑concurrency, ad‑hoc, and batch workloads.
