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.
This article details Gaode Maps' end‑to‑end lakehouse solution for handling high‑frequency, high‑volume trajectory data, covering the challenges of real‑time visibility, multi‑scenario queries, storage cost, and data silos, and describing the layered storage architecture, performance validation, and future expansion plans.
This article explains how dbt drives automated data modeling and governance, how DataOps practices bring agility and control to data projects, and how StarRocks’ lakehouse architecture enables real‑time and batch analytics, illustrated with concrete workflows, version‑control conventions, and enterprise case studies.
Facing Postgres overload and costly Snowflake queries, Fresha rebuilt its analytics platform by introducing StarRocks as a unified SQL entry point, combining federated lakehouse queries with high‑performance internal tables, which reduced homepage query latency to around 200 ms and achieved minute‑level data freshness across real‑time, historical, and search workloads.
This article explains how StarRocks mitigates the hidden risks of massive one‑time data imports in a storage‑compute separated architecture by redesigning the write path to spill to local disk, merge centrally, and write to object storage, resulting in fewer small files, higher write throughput, and more stable query performance.
A reproducible evaluation of StarRocks using the open‑source Coffee‑shop Benchmark shows that across 500 M, 1 B and 5 B row scales, StarRocks completes 17 complex join and aggregation queries 2–10× faster and with significantly lower cost than ClickHouse, Snowflake and Databricks, demonstrating superior performance and cost efficiency for analytical workloads.
StarRocks 4.0 introduces FlatJSON, a columnar storage and execution engine that converts high‑frequency JSON fields into native columns, dramatically reducing I/O and CPU costs and enabling JSON queries to run with performance close to that of traditional columnar data.
This article details Cisco Webex's migration from a complex Pinot‑Trino OLAP stack to StarRocks, covering the challenges of the legacy system, the step‑by‑step migration process—including storage, compute, and SQL dialect transformation—and the resulting performance gains, cost reductions, and operational improvements.
StarRocks 3.5 introduces a cluster‑level snapshot mechanism that automates backup to object storage, supports minute‑level recovery, and integrates with Kubernetes via Helm charts to streamline disaster‑recovery workflows for high‑availability workloads.
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.
