The article examines how the rise of AI agents is reshaping enterprise data infrastructure, tracing software evolution from rule‑based systems to lakehouses and arguing that real‑time OLAP engines with sub‑second latency, hybrid search, and semantic schemas will become the core of the new Agent‑centric stack.
This article introduces ClickHouse as an open‑source column‑store OLAP database, outlines its core features, explains its distributed and cloud‑native architectures—including SharedMergeTree for serverless operation—presents benchmark results, compares community and enterprise editions, and answers common questions about its future direction.
This article reviews the latest developments in data lakes, including trend analysis, overall architecture, data integration methods, Lakehouse core capabilities, open design principles, stream‑batch unified processing, real‑time OLAP, and lake‑internal warehousing, highlighting how these advances reduce complexity and cost while improving data sharing and performance.
This article presents a comprehensive overview of the Apache‑incubated Paimon project, covering its evolution from Flink Table Store, the current features of primary‑key and log tables, management tools such as snapshots, tags and branches, performance optimizations for Flink and Spark, and a detailed roadmap of upcoming functionalities.
REDck, a cloud‑native real‑time data warehouse built on open‑source ClickHouse, overcomes the original MPP architecture’s scaling and maintenance limits by separating compute and storage, introducing unified metadata, multi‑level caching, bucket‑based sharding, and distributed transaction support, delivering petabyte‑scale, 99.9% availability and ten‑fold cost and performance gains for Xiaohongshu’s diverse workloads.
REDck is a cloud‑native, storage‑compute separated real‑time OLAP data warehouse derived from ClickHouse that addresses scalability, operational cost, and reliability challenges through a unified metadata service, object‑storage optimizations, multi‑level caching, distributed task scheduling, and two‑phase commit transactions.
Xiaohongshu built REDck, a cloud‑native, storage‑compute separated real‑time OLAP warehouse on ClickHouse, addressing scaling, cost, and reliability challenges through a unified metadata service, object‑storage optimizations, multi‑level caching, distributed task scheduling, bucketing, and exactly‑once transaction support.
This article explains why lake‑warehouse fusion is needed, describes the challenges of integrating real‑time data warehouses with data lakes, introduces a new StarRocks‑based architecture that supports real‑time ingestion, cooling, offline loading, and adaptive hot‑cold query rewriting, and outlines future plans and Q&A.
REDck is a cloud‑native, real‑time OLAP data warehouse built on ClickHouse that adds elastic compute and storage scaling, object‑storage optimizations, multi‑level caching, and exactly‑once ingestion, delivering petabyte‑scale interactive analytics with ten‑fold CPU efficiency, ten‑fold cost reduction, and 99.9% availability.
Songguo Mobility’s data‑center team migrated from a fragmented Impala‑Kudu‑ClickHouse stack to a unified StarRocks‑based real‑time OLAP architecture, iterating through three versions to solve scalability, latency, and maintenance challenges while supporting minute‑level dashboards for orders and vehicle analytics.
This article analyzes the limitations of traditional Lambda and Kappa big‑data architectures for online‑school behavior‑feature pipelines and presents a Flink + Hologres solution that provides unified real‑time OLAP and high‑concurrency point‑query services, including design choices, implementation details, and performance results.
This article details BTC.com’s journey from a legacy batch‑oriented analytics stack to a modern real‑time OLAP architecture using Flink, ClickHouse, Kafka, and Kubernetes, highlighting the business drivers, technical choices, architectural evolution, optimizations, and future directions.
This article details BTC.com’s journey in building a real‑time OLAP platform for blockchain data, covering business background, challenges, architectural evolution, technology choices such as Flink and ClickHouse, optimization techniques, monitoring, and future directions.
This article details AutoHome's use of Apache Kylin as its core OLAP engine, covering its development timeline, architecture, large‑scale cube deployment, performance optimizations, cluster upgrade experiences, and future directions such as real‑time OLAP and cloud‑native deployment.
This article describes how iQIYI evaluated various OLAP engines, selected Apache Druid for real‑time analytics, detailed its architecture, identified performance bottlene‑cks in Coordinator, Overlord and indexing, applied configuration and resource‑allocation optimizations, and built a user‑friendly RAP platform to democratize real‑time data analysis.
This article details Autohome's use of Apache Kylin as its core OLAP engine, covering its architecture, large‑scale Cube deployment, real‑world business applications, a series of performance and operational optimizations, cluster upgrade experiences, and upcoming plans for real‑time OLAP and cloud‑native evolution.
iQiyi replaced its offline OLAP stack with Apache Druid, leveraging its real‑time, multi‑dimensional engine and a five‑component architecture, while solving coordinator and overlord bottlenecks, optimizing indexing resources, adopting KIS mode, and building the self‑service RAP platform that now powers thousands of low‑latency dashboards.
This article provides a comprehensive overview of Apache Druid, covering its real‑time OLAP design, core features, six‑component architecture, segment storage model, data ingestion pipelines (including Tranquility and Kafka), native and SQL query interfaces, and practical tuning tips with code examples.
