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.
Facing high costs and scaling limits with ClickHouse, we migrated a 4000‑core, 500TB OLAP workload to StarRocks, achieving 40% cost reduction, 50% storage savings, and up to 30× query speedups through storage‑compute separation, materialized‑view rewrites, and extensive performance tuning.
This article details Tongcheng Travel's migration from Hive/Kudu/Hudi to Paimon for lakehouse integration, highlighting a 30% resource reduction, three‑fold write speed gains, significant query acceleration via StarRocks, the end‑to‑end architecture across ODS‑DWD‑DWS‑ADS layers, and future roadmap plans.
This guide walks through the core concepts of Apache Hive, its architecture and key features, then shows how to integrate Hive with StarRocks via the Hive Catalog, construct ODS/DWD/DWS/ADS tables, enable DataCache, use materialized views, and handle automatic partition detection for fast lakehouse analytics.
This article details Ele.me's journey in evolving its real‑time data warehouse, covering the original 1.0 architecture, the 2.0 lakehouse redesign with Paimon and StarRocks, performance evaluations of lake formats and query engines, and the roadmap toward a 3.0 streaming lakehouse solution.
This tutorial walks you through the fundamentals of Apache Iceberg, its architecture and key features, explains why it’s advantageous for lakehouse workloads, and provides a step‑by‑step Docker‑Compose setup to integrate Iceberg with StarRocks for fast, ACID‑compliant analytics on real‑world taxi data.
StarRocks 3.3.1 introduces a cloud‑native persistent index that moves index files to object storage, eliminates local‑disk constraints, and supports elastic scaling, delivering up to ten‑fold latency improvement over local‑disk indexes in elastic scheduling while matching performance in batch and real‑time imports.
This guide provides a comprehensive overview of Apache Paimon’s architecture, key features, and advantages, explains how to integrate it with StarRocks for real‑time lakehouse analytics, and walks through a complete quick‑start setup including component installation, Flink and Kafka deployment, data ingestion, table creation, and query execution with time‑travel support.
Pinterest migrated its Partner Insights analytics from Druid to StarRocks, achieving a 50% reduction in p90 latency, a six‑fold cost‑performance improvement, and simplified data ingestion, illustrating the benefits of a modern MPP database for real‑time ad analytics.
Kingsoft Office migrated its reporting platform from a multi‑engine stack to StarRocks 3.0, achieving a 48.84% performance gain, halving query latency, reducing operational costs, and improving resource utilization while supporting storage‑compute separation and seamless Trino SQL compatibility.
The article examines the rapid rise of lakehouse architecture, its market momentum, core components—including storage, metadata, table formats, and compute layers—compares Iceberg, Hudi, and Delta Lake, discusses the shift from HDFS to object storage, and outlines the strategic importance of lakehouses for AI-driven data management and future data infrastructure trends.
