KaptureCX migrated its core analytics from ClickHouse to StarRocks, introduced RisingWave and Kafka for CDC, and achieved millisecond‑level query latency, a reporting cycle cut from weeks to one day, and a solid data foundation for AI‑driven services.
Traditional databases were built for low‑frequency, human‑driven queries, but AI agents generate dozens of concurrent, sub‑second queries that expose architectural limits, and StarRocks addresses these challenges with self‑healing optimization, real‑time data pipelines, extreme concurrency handling, and seamless lakehouse access.
This article explains the core I/O components of StarRocks—Tablet, Fragment, Pipeline, Morsel, ScanOperator, ChunkSource, ScanTask, and ChunkBuffer—showing how they work together to achieve high‑performance, low‑latency query execution in a compute‑storage separated architecture.
This case study details how Vivo’s marketing automation platform evolved its data‑driven architecture—from a Presto‑based wide‑table design, through a Bitmap optimization, to a StarRocks migration—addressing performance bottlenecks, reducing resource costs, and enhancing data security.
This article dissects the full advertising funnel, explains why CTR and eCPM are critical, and demonstrates how StarRocks combined with Flink can deliver minute‑level real‑time monitoring, material selection, anomaly alerts, A/B testing, and a successful migration from Druid for massive ad‑tech workloads.
This roundup compiles the latest releases across major data platforms—including ClickHouse, Apache Fluss, Delta Lake, StarRocks, Apache Pulsar and DolphinScheduler—highlighting version numbers, key feature additions, security fixes, and emerging trends shaping the big‑data ecosystem.
Fanatics unified its fragmented data stack by building a StarRocks‑powered Lakehouse on Apache Iceberg, replacing Redshift, Snowflake, Athena, and Druid, which cut costs by up to 95%, delivered sub‑second dashboard queries on petabyte‑scale data, and enabled real‑time and historical analytics on a single platform.
This article details the deep integration of StarRocks with Apache Paimon, describing the unified architecture, version evolution, performance enhancements, time‑travel queries, native readers/writers, distributed planning, and future roadmap for achieving lakehouse‑native analytics at scale.
During Double‑11 mega‑sales, Taobao Group faced exploding OLAP query traffic, costly data sync pipelines, and slow near‑real‑time analytics, so they unified real‑time and batch data in Paimon, leveraged StarRocks for high‑performance lake queries, tuned cluster settings, and saved nearly ten‑million yuan annually while cutting refresh latency by 80%.
This article details the evolution of a data warehouse at RenliJia from a MaxCompute‑centric setup to a modern lakehouse using StarRocks, Paimon, Flink, and Fluss, describing design goals, technical evaluations, implementation steps for offline, OLAP, and real‑time workloads, and the challenges and future plans that emerged.
This article explains how Taotian Group unified real‑time and offline data using Paimon as lake storage and StarRocks for high‑performance OLAP, eliminating costly sync pipelines, cutting refresh time by about 80%, saving nearly ten million yuan annually, and detailing the architecture, cluster safeguards, configuration tweaks, monitoring, and future roadmap for large‑scale promotional events.
This article details Gaode Maps' end‑to‑end lakehouse solution for massive, high‑frequency trajectory data, covering the challenges of real‑time visibility, query performance, and storage cost, and explaining how a hot‑warm‑cold tiering architecture built on Apache Flink, Paimon, StarRocks, Redis and Lindorm delivers millisecond‑level queries while cutting storage expenses.
Veteran data‑warehouse expert explains how the Paimon‑StarRocks stack solves the write‑read split, cuts storage costs, and delivers real‑time analytics, comparing it with Hudi, Iceberg, ClickHouse and Trino, and shows why leading Chinese tech firms are adopting this lakehouse architecture.
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.
StarRocks and Apache Paimon have been progressively integrated across multiple releases, enabling a unified lakehouse architecture that supports multi-source federated analysis, time-travel queries, native readers/writers, distributed planning, and advanced profiling, while delivering performance gains that bring Paimon query speed on par with native StarRocks tables.
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.
Ctrip migrated its massive User Behavior Tracking system from ClickHouse to a compute‑storage separated StarRocks cluster on Kubernetes, achieving millisecond‑level query latency, halving storage usage, reducing node count, and sustaining millions‑of‑rows‑per‑second write throughput while simplifying scaling and operations.
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.
This article chronicles a ten‑year evolution of a self‑built big data platform—detailing early Hadoop clusters, successive migrations to Spark, Hive, Hudi, and StarRocks, the operational challenges encountered, and the comprehensive shift to Alibaba Cloud EMR Serverless that delivered significant cost, performance, and stability gains while outlining future intelligent‑ecosystem plans.
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.
Since its 2016 launch, Alibaba Cloud EMR has transformed from a basic open‑source Hadoop service into a high‑performance, AI‑enabled big‑data platform, delivering optimized I/O, vectorized processing, and integrated AI functions such as natural‑language SQL, StarRocks and Spark enhancements, while supporting diverse industry 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.
This case study details how Shuhe Technology, a leading fintech company, overcame data redundancy, low resource utilization, and slow reporting by adopting Alibaba Cloud EMR Serverless StarRocks for a unified, real‑time data warehouse, achieving standardized data pipelines, cost savings, and minute‑level decision latency.
This article explains how the Tianji advertising platform consolidated heterogeneous MySQL, ClickHouse, and TiDB data sources into a single StarRocks data warehouse, addressing data silos, real‑time performance, and query complexity while improving accuracy, latency, and development efficiency.
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.
Facing minute‑level decision demands and billions of marketing events during Taobao's Flash Sale, the Ele.me data team built a real‑time lakehouse with StarRocks and Paimon, leveraging asynchronous materialized views, RoaringBitmap de‑duplication, and resource isolation to achieve sub‑second query latency, lower storage costs, and stable high‑concurrency.
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.
This article explains the roles of StarRocks FE and BE nodes, details the metadata stored in FE, describes the leader‑follower‑observer architecture, and shows how BDB JE replication, journal logs, and checkpoint mechanisms ensure metadata synchronization and durability even after node failures.
This case study details how Hypergryph leveraged Alibaba Cloud EMR Serverless StarRocks, Flink, and Kafka to replace a ClickHouse data warehouse with a high‑performance, elastic, and easy‑to‑operate real‑time analytics platform that dramatically improved query speed, stability, operational efficiency, and cost for their gaming business.
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.
The article explains the purpose, configuration, and core implementation of StarRocks' TabletChecker component, detailing how it periodically scans OlapTable tablets, evaluates their health through multiple checks, and hands unhealthy tablets to the TabletScheduler for repair.
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 article details how Quark’s data platform consolidated multiple analytics engines into a unified StarRocks‑based OLAP solution, covering business background, engine selection, architecture redesign, performance tuning, operational practices, and future plans for scalability and reliability.
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.
StarRocks 3.5 introduces a cluster‑level Snapshot backup for fast recovery, a bulk‑load optimization that reduces small files and compaction cost, smarter partition management with time‑based merging and TTL, multi‑statement transactions with full ACID guarantees, low‑cardinality dictionary support for lake tables, and several security and performance upgrades.
This article details Shopee's Data Infra team's use of the Paimon data lake to build near‑real‑time warehouses, accelerate ODS layers, implement a task‑diagnosis system, and create a reconciliation platform, while sharing future plans and a Q&A session.
This guide outlines the StarRocks SRCA certification exam, highlights essential study resources, breaks down the core topics such as architecture, data import/export, SQL optimization and performance tuning, and offers practical tips, mock‑exam details, and personal experience to help candidates succeed.
This article examines how Xiaohongshu replaced its costly Lambda architecture with a real‑time lakehouse built on Iceberg, Paimon, Spark, and StarRocks, achieving minute‑level latency, higher data quality, lower resource consumption, and dramatically faster query performance.
This article details how Alibaba's A+ traffic analysis platform tackled trillion‑row log ingestion and high‑concurrency queries by redesigning storage with Paimon, leveraging Flink for real‑time ingestion, and using StarRocks for fast lake analytics, ultimately reducing query latency from minutes to seconds.
StarRocks MCP Server provides a universal adapter that lets large language models like Claude, OpenAI, and Gemini execute SQL queries directly against StarRocks, simplifying data Q&A, intelligent analysis, and automated reporting by eliminating the need for bespoke plugins or complex prompt engineering.
Grab replaced its fragmented Grafana‑Superset stack with a StarRocks‑backed Iris platform, achieving over ten‑fold query speedups, 40% lower resource usage, and a unified real‑time and historical data store for Spark observability across its Southeast Asian super‑app ecosystem.
Facing a flood of data from over 8,000 communities, the Bifeng service team migrated from a monolithic storage‑compute architecture to a StarRocks‑based storage‑compute separation solution, achieving lower costs, higher resource utilization, faster queries, and improved SLA across their property management platform.
This article provides a comprehensive technical overview of StarRocks' query optimizer, covering its evolution, core architecture, multi‑stage optimization pipeline, key optimizations such as multi‑join colocate, low‑cardinality global dictionary, MV union rewrite, and advanced mechanisms like cost‑estimation fixes, query feedback, adaptive execution, runtime filters, join‑reorder strategies, and SQL plan management.
This guide explains how to design a comprehensive monitoring and alerting framework for StarRocks, covering resource usage, service availability, and business continuity with practical PromQL queries and troubleshooting steps.
This article explains the fundamentals of Retrieval‑Augmented Generation, demonstrates how to create and query vector indexes using StarRocks, shows how DeepSeek provides embeddings and answer generation, and walks through a complete end‑to‑end RAG pipeline with code examples and a web UI.
JD Logistics transformed its one‑stop self‑service analytics platform, UData, by migrating from an integrated storage‑compute architecture to a storage‑compute separated design powered by StarRocks, achieving sub‑10‑second P95/P99 query latency, reducing storage costs by 90%, and cutting compute expenses around 30% while supporting massive data volumes.
The article details how StarRocks extends the Apache ORC C++ library to decrypt column‑level encrypted ORC files, describing the file hierarchy, AES‑128‑CTR key handling, the query‑time master‑key retrieval, a decorator‑based decryption/decompression pipeline, and the block‑skip‑read mechanism that enables efficient predicate push‑down.
This step‑by‑step guide explains how to use EMR Serverless Spark together with the StarRocks Spark Connector to create a workspace, upload the connector JAR, configure network connections, create databases and tables in StarRocks, and perform read/write operations via SQL sessions, Notebook sessions, or batch Spark jobs, complete with code examples and UI screenshots.
NAVER migrated its massive analytics platform from ClickHouse to StarRocks, achieving dramatic improvements in multi‑table JOIN performance, real‑time aggregation speed, and horizontal scalability while simplifying data integration across heterogeneous sources on a Kubernetes‑based architecture.
This article details iQIYI's transition from Impala+Kudu and ClickHouse to StarRocks, describing the OLAP architecture, performance gains of up to 400% in advertising workloads, the technical challenges of data consistency, lake‑warehouse fusion, operational scaling, and the step‑by‑step migration process using a dual‑run platform.
Xiaohongshu tackled low reuse of APP tables, limited scalability of single-table BI datasets, and poor dashboard query performance by introducing logical datasets and materialized views, which enable query pruning, reduce data redundancy, and accelerate BI queries, achieving up to 80% latency reduction and higher hit rates.
The article details how Xiaohongshu's RedBI self‑service analytics platform transformed its architecture by integrating StarRocks and Iceberg, replacing ClickHouse‑based storage with Parquet, introducing DataCache, Z‑Order sorting and intelligent key selection, achieving a three‑fold P90 query speed improvement, sub‑10‑second latency, and halving storage consumption.
This article explains the principles and practical implementation of vector retrieval in StarRocks, covering approximate nearest‑neighbor algorithms, index design, query planning, performance optimizations, real‑world case studies, and future challenges, showing how query latency dropped from 15 seconds to 2 seconds while cutting costs to a third.
58.com integrated the StarRocks analytical engine into its data‑exploration platform, replacing Spark/Hive, to overcome minute‑level latency, and after a year of migration achieved over 20× query speedup, 98%+ success rate, and solved numerous Spark‑StarRocks compatibility issues while also moving the service to the cloud.
DMALL upgraded its big‑data platform by adopting StarRocks 3.x with compute‑storage separation, lakehouse external tables, and Kubernetes deployment, achieving 20% higher compute utilization, 40% lower storage cost, faster cluster provisioning, and notable improvements in development and operations efficiency.
This tutorial walks you through using Alibaba Cloud EMR Serverless StarRocks and Apache Paimon to create a cost‑effective, high‑performance game player profiling and behavior analysis platform, covering data import, materialized view creation, DWD/ADS layer construction, and lakehouse integration.
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.
The article details the background, architectural analysis, multi‑tenant redesign, engine selection enhancements, compatibility adaptations, stability fixes, containerized deployment, performance optimizations, and measurable business outcomes of the Intelligent Acceleration Engine upgrade using Apache Kyuubi and StarRocks within the 58 big data platform.
The article details the background, architectural challenges, and comprehensive redesign of the Smart Acceleration Engine—including multi‑tenant support, cross‑datacenter scheduling, enriched engine selection, parsing and forwarding enhancements, compatibility adaptations, stability fixes, containerized deployment, and performance gains—demonstrating significant operational improvements and future directions for the platform.
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 article walks through StarRocks' materialized view refresh mechanisms, explains the various refresh triggers, details the refresh workflow, and introduces new parameters that allow selective refreshing of only changed data to avoid costly full‑partition refreshes.
This guide introduces StarRocks, a high‑performance MPP database, explains its architecture and typical use cases, walks through a Docker‑based quickstart, shows how to create databases and tables, load NYC crash and weather datasets via Stream Load, and demonstrates analytical SQL queries that reveal traffic‑accident patterns under different weather conditions.
This article details Ximalaya Live's transition from an offline‑centric data warehouse to a real‑time lakehouse using Flink, Paimon, and StarRocks, covering business background, architectural challenges, technology evaluation, implementation steps, encountered issues, performance gains, and future expansion plans.
Shopee’s deployment of StarRocks across DataService, DataGo, and DataStudio demonstrates that its vectorized engine, cost‑based optimizer, and materialized‑view caching can query Hive, Iceberg, Delta Lake and Hudi up to 20,000× faster than Presto, cutting CPU usage and delivering consistently lower latency for complex analytics.
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.
Qimao, a Shanghai‑based cultural entertainment internet firm, details its 20 PB big‑data architecture built on StarRocks, Flink, Hive, and Redis, covering data ingestion, real‑time processing, audience selection, metric anomaly drill‑down, 730‑day aggregation, and future plans for metric acceleration and full‑link data governance.
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.
This article details how Tencent Music’s data‑infrastructure team migrated thousands of ClickHouse and Druid nodes to a StarRocks compute‑storage‑separated lakehouse, achieving 40‑50% cost reduction while maintaining query performance, and shares the technical challenges, solutions, and best‑practice recommendations gathered during the process.
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.
StarRocks, an open‑source, high‑performance MPP analytical database under the Linux Foundation, offers vectorized engines, CBO optimizer, materialized views, and storage‑compute separation, integrates with BI tools and data lakes, and demonstrates superior query speed in benchmark tests against ClickHouse, Druid, and Trino.
StarRocks combines vectorized execution, a new cost‑based optimizer, materialized views, a real‑time storage engine, pipeline execution, and distributed joins to deliver a unified, high‑performance OLAP solution that supports both traditional and lakehouse analytics while reducing operational complexity.
This article examines how StarRocks, a next‑generation MPP database, leverages AWS Graviton3 instances to achieve over 30% query speed improvement and 15% cost reduction compared with x86 C6i instances, detailing benchmark methodology, hardware specs, SIMD optimizations, and real‑world OLAP results.
This article provides a practical deep‑dive into StarRocks and Apache Paimon, covering data‑lake fundamentals, the technical advantages of both platforms, performance gains over traditional engines, step‑by‑step migration strategies, deployment options on Alibaba Cloud EMR, and future roadmap plans.
This article presents a practical overview of StarRocks and Apache Paimon data‑lake capabilities, explains their performance advantages, details migration strategies from Trino/Presto and other engines, describes cluster‑to‑cluster migration, and outlines future roadmap for integration and optimization.
The article outlines a cross‑cluster upgrade to StarRocks 2.5.13, evaluating resource and stability costs, and presents two migration schemes—using external tables and a Flink connector—along with planning, parallel execution, validation steps, and results showing successful migration of over 10 TB at 2 Gb/s across ten nodes, while noting future automation and CDC enhancements.
StarRocks 3.3 introduces a comprehensive set of enhancements—including maturity levels, ARM‑optimized performance, advanced caching, materialized‑view rewrites, storage optimizations, and expanded lakehouse ecosystem support—that together boost stability, query speed, and usability for large‑scale analytics workloads.
This article details JD Retail's transition from a complex Lambda architecture to a unified real‑time data pipeline using Flink, Hudi, and StarRocks, addressing data completeness versus latency, reducing maintenance costs, improving storage efficiency, and delivering faster, more consistent analytics for business users.
This article explains StarRocks' compaction process that merges multiple data versions into larger files to reduce I/O, details the scheduler and executor roles, shows how to monitor and control compaction via SQL commands, and provides tuning parameters and best‑practice recommendations for optimal performance.
This article provides a detailed technical comparison between StarRocks and Trino, covering their shared MPP architecture, cost‑based optimizer, pipeline execution, ANSI SQL support, differences in vectorized execution, materialized view capabilities, caching systems, data source connectors, benchmark results, high‑availability designs, join algorithms, and real‑world user case studies.
StarRocks combines extreme query speed and a unified architecture to deliver a lakehouse solution that separates storage and compute, supports multi‑warehouse resource isolation, offers Trino compatibility, materialized‑view acceleration, and cost‑effective scaling, making it suitable for real‑time analytics, data‑lake queries, and traditional OLAP workloads.
Apache Iceberg offers a modern, ACID‑compliant table format for data lakes with features like hidden partitions and schema evolution, while StarRocks provides high‑performance query acceleration, metadata caching, and distributed planning to address Iceberg’s latency challenges, enabling seamless lake‑warehouse integration and real‑time analytics.
Tencent Games tackled the low accuracy of AI‑generated SQL in production by combining large language models with a StarRocks lake‑warehouse, introducing a semantic layer, async materialized views, and an agent‑based multi‑intelligence framework, ultimately raising one‑shot SQL correctness to 89% and cutting delivery time from 2 hours to 0.33 hours.
This article explains how to build a high‑performance data‑lake analytics stack by combining StarRocks with Apache Paimon, covering direct queries, Data Cache acceleration, and asynchronous materialized views, and presents benchmark results that show StarRocks achieving up to 4.3× faster query speeds than Trino and significant latency reductions with caching and materialized views.
This article details Tencent Video’s data business, describing the design and implementation of its metrics middle platform and lake‑warehouse integration, covering architecture, governance, consistency, timeliness, usability, cost optimization, and future plans, with insights into technology choices such as Iceberg, StarRocks, and MQL.
This comprehensive guide walks you through StarRocks production deployment, covering capacity planning, hardware specs, environment setup, table modeling, partitioning, bucketing, index selection, data import best practices, query optimization, monitoring, and resource‑group configuration, all backed by concrete SQL examples and configuration commands.
This article explains the concept of a metrics layer, why StarRocks is suited for building such platforms, and presents detailed case studies from Airbnb, a major bank, and a leading restaurant chain, while comparing three modeling paradigms and outlining the future vision for materialized views.
This article analyzes the challenges of building real‑time and batch risk‑control features, compares Lambda and Kappa architectures, evaluates storage‑unified and compute‑unified solutions, and details how StarRocks was selected, validated, and deployed to achieve high‑performance, low‑latency feature serving in a financial context.
The article details how the Xiaohongshu data warehouse team integrated StarRocks into their offline processing pipeline, replacing Spark for heavy Cube calculations, which reduced job execution from hours to minutes, cut resource consumption by over 90%, advanced daily data output by 1.5 hours, and lowered refresh cost by more than 99%.
Ctrip’s ticket business rebuilt its multi‑engine metrics platform by consolidating ClickHouse, Kylin, and Presto into a single StarRocks database, introducing temporary tables, materialized views, and streamlined ETL, which cut complex query times from minutes to seconds and doubled user traffic.
