This article explains ElasticSearch’s architecture and core mechanisms—including its reliance on Lucene segments, inverted indexes, stored fields, document values, caching, shard routing, and scaling strategies—while answering common questions about wildcard matching, index compression, and memory usage.
This article explains how a third‑generation metric platform replaces traditional ETL‑heavy data‑warehouse pipelines with a semantic‑driven NoETL approach, reducing cost, improving quality and efficiency, and delivering automated, self‑service analytics for both IT and business users.
This article explains how the Dongchedi team designed, implemented, and monitored a comprehensive indicator system within a petabyte‑scale data warehouse, covering standards, metadata management, model construction, quality monitoring, and diverse application scenarios to improve data reliability and business insight.
When faced with a business requirement to filter up to 100 000 records from a pool of tens of millions and then sort and de‑duplicate them, this article explores four technical solutions—multithreaded ClickHouse pagination, Elasticsearch scroll‑scan, a combined Elasticsearch‑HBase approach, and RediSearch with RedisJSON—detailing their design, implementation, performance testing, and trade‑offs.
This article shares how China Unicom Digital Technology leverages DataOps to build an integrated data governance, research and development, and operations capability, outlining challenges, methodological considerations, a seven-step governance framework, and a multi-center collaborative mechanism to achieve sustainable data-driven value.
Bloom filters are space‑efficient probabilistic structures that answer “definitely not” or “maybe” membership queries, with a controllable false‑positive rate derived from bit array size, element count, and hash functions, and can be implemented via Guava’s Java library, Redisson’s Redis wrapper, native Redis modules, or custom bitmap code, dramatically reducing memory usage and latency in large‑scale systems such as URL deduplication or user‑product checks.
The article presents Turing Data Analysis (TDA), a self‑service BI platform that replaces fragile traditional pipelines with a unified DWD‑based data model, drag‑and‑drop analytics, multi‑engine query optimization and caching, delivering sub‑10‑second queries on billions of rows, fine‑grained permissions, and rapid dashboard creation, while reporting significant usage growth and outlining AI‑driven future enhancements.
Data middle platforms transform raw enterprise data into reusable assets by integrating collection, storage, processing, governance, and service layers, enabling faster deployment, consistent metrics, improved data quality, and business value across digital transformation, while addressing challenges like siloed data, low efficiency, and inconsistent standards.
This article shares Xiaomi's real‑world challenges and solutions when building a new Spark 3.1‑based data platform, covering Multiple Catalog implementation, Hive‑to‑Spark SQL migration, automated batch upgrades, performance and stability optimizations, and future roadmap for vectorized execution.
This article analyzes the challenges of commercial real‑time data processing—such as stability, multi‑stage computation, and frequent schema changes—and explains how the RTS platform provides end‑to‑end managed solutions, auto schema handling, primary‑secondary redundancy, experiment‑first deployment, and metadata generation to unlock high‑velocity data value for advertising operations.
This article presents Li Auto’s end‑to‑end data integration journey, detailing the evolution of its data platform, the challenges of heterogeneous sources, and how a unified Flink‑on‑K8s solution with cloud‑native architecture, operator management, monitoring, and checkpointing addresses batch‑stream convergence and future scalability.
This article explores Apache Kafka’s core storage architecture, explaining how sequential append‑only writes, sparse indexing, segmented log files, and a leader‑based replication mechanism together enable high‑throughput, reliable, and scalable event streaming for massive data workloads.
This article outlines the core capabilities of data-driven development, the background and architecture of DataOps, its research challenges and focus areas, and explores future directions such as data virtualization, platform governance, and data value assessment, providing a comprehensive overview of DataOps practices.
This article introduces a comprehensive ClickHouse training program that covers fundamental concepts, architecture, installation, distributed cluster design, data import, performance tuning, and includes a detailed list of 33 video modules and additional recommended reading resources for large‑scale data analytics.
DataFunCon 2024 Shanghai brings together leading experts from AI, big data, cloud computing, and industry sectors to discuss cutting‑edge technologies, large‑model applications, intelligent operations, and digital transformation across automotive, healthcare, finance, retail, and entertainment.
The DataFunCon 2024 Shanghai conference brings together leading experts from academia and industry to discuss cutting‑edge topics such as large language models, AI‑driven operations, data governance, digital transformation, and emerging applications across automotive, finance, retail, and entertainment sectors.
This article examines ByteDance's DataOps journey, detailing the data‑engineering challenges faced, the concrete solutions and productization through the DataLeap platform, the metrics and best‑practice framework adopted, and the future directions involving AI‑assisted development and open‑source collaboration.
OPPO's data architects combined their self‑developed Shuttle service with Alluxio to double performance, halve system pressure, and double throughput, while building a unified Data & AI lakehouse that integrates structured and unstructured data, metadata management, real‑time ingestion, and cloud cost reductions.
This guide explains how to efficiently load one billion 1‑KB log entries from HDFS or S3 into MySQL by analyzing B‑tree limits, using batch inserts, choosing the right storage engine, sharding tables, optimizing file reading, and coordinating tasks with Redis, Redisson, and Zookeeper.
DataFunCon2024 Shanghai brings together leading experts from AI, big data, cloud computing and various industries such as automotive, biotech, retail, finance and entertainment to share cutting‑edge research, practical case studies and future trends through a series of keynote speeches, panels and technical sessions.
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.
This article explains the value of user profiling platforms, outlines their core functions, presents a layered architecture with open‑source options, and details engineering optimizations—from wide‑table design to BitMap caching and task‑mode execution—while also discussing current industry trends.
This guide explains how to use Alibaba Cloud Function Compute to inspect Kafka message keys and automatically perform insert, update, or delete operations on MongoDB, detailing the architecture, advantages, prerequisites, step‑by‑step deployment, and current limitations.
This article explores data collection governance by distinguishing data quality compliance from reasonableness, introduces a comprehensive quality review tool suite—including visual inspection, intelligent judgment, and self‑diagnosis—detailing its architecture, key techniques, and practical case studies for ensuring reliable data metrics.
This article presents a comprehensive case study of Cao Cao Mobility's transition from a traditional Lambda architecture to an enterprise‑grade real‑time data warehouse built on Hologres and Flink, detailing business background, pain points, architectural design, performance optimizations, metadata management, and future development directions.
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%.
This article details the design and implementation of a user data warehouse at 58.com, covering data warehouse fundamentals, user profiling concepts, multi‑layer architecture, modeling methods, ETL migration from Hive to Spark, data quality assurance, and the resulting achievements.
This article details Didi's comprehensive big‑data asset governance platform, covering its architectural layers, Hadoop and Elasticsearch governance practices, health‑score models, lifecycle recommendations, and future plans for automated and intelligent data governance to reduce cost and manual effort.
This article explains the performance and development challenges of end‑to‑end AI/Big Data workflows on Kubernetes and shows how combining Fluid’s data orchestration with Vineyard’s zero‑copy sharing can dramatically improve efficiency, followed by a step‑by‑step tutorial with code examples.
The article reviews Flink 1.19’s new features, highlighting SQL capability enhancements such as custom source parallelism, TTL hints, and MiniBatch support for regular joins, as well as runtime dynamic parallelism for batch jobs and flexible checkpointing intervals for different data sources.
This article details ByteDance's large‑scale evolution of Spark Shuffle to a cloud‑native architecture, describing background, stability and mixed‑resource scenarios, challenges such as CPU and I/O limits, custom ESS enhancements, shuffle throttling, spill‑split mechanisms, and the Cloud Shuffle Service with its push‑based design and performance gains.
This article explains how Ping An Bank transformed its retail finance model from product‑centric to customer‑centric using a five‑in‑one data‑driven approach, the KYC/KYP/KYATO methodology, and the StarRocks analytics platform to build the Smart Bank 3.0 architecture, CDP, and real‑time metric layers.
This article details how Alipay's data engineering team applied Elon Musk's five‑step work method to completely refactor a decade‑old merchant billing system, reducing overall complexity by over 60%, improving timeliness by an hour, cutting storage and compute costs by a third, and dramatically lowering operational and maintenance burdens.
This article presents a comprehensive vehicle‑IoT big‑data platform solution that outlines required capabilities, describes a DolphinDB‑based architecture, shares a real‑world case of 1.8 × 10⁸ writes per second, and provides step‑by‑step deployment and query scripts for rapid verification.
This article presents a comprehensive, scenario-driven data governance practice at Guoxin Securities, covering the industry's pain points, a three‑layer governance framework, detailed implementations for data standards, metadata, data quality, data modeling, and data security, and outlines future directions for intelligent and measurable governance.
This article presents OPPO's smart data lakehouse solution, describing its massive EB‑scale architecture, the integration of batch and streaming engines, the Glacier service for table management, schema‑adaptive ingestion, performance optimizations, and future technical road‑maps for unified data processing.
This article introduces KwaiBI, the internal data analysis product of Kuaishou, outlines its five major functional areas, details the performance challenges of large‑scale analytics, and presents a comprehensive set of optimization techniques—including cache warming, query rewriting, materialized acceleration, and the Bleem lake‑house engine—along with future directions and a brief Q&A.
The article explains Flink’s metrics architecture—core concepts, reporter interfaces, built‑in and custom metric types, elastic plugin design, and scheduled reporting—illustrated with a consumption‑latency example, and shows how Didi uses these metrics for real‑time UI curves, alerts, and intelligent task diagnosis.
This tutorial explains how to install and configure Apache Flink in three deployment modes—Standalone, Hadoop YARN, and Kubernetes—covering node preparation, configuration files, package distribution, job submission, and monitoring through the Flink Web UI, with full command‑line examples and code snippets.
Apache Paimon 0.7.0 introduces significant improvements such as optimized lookup join handling, new CDC functionalities, and tighter Spark/Hive integration, while also highlighting practical considerations for using lake‑table lookups in production environments.
Ant TuGraph’s open‑source graph computing engine, led by Fang Zhihong, will be introduced covering its development history, architectural design, technical principles, integrated stream‑batch‑graph processing capabilities, real‑world large‑scale graph use cases, and future roadmap, offering insights into design, implementation, and value.
Facing exploding data volumes and rising cluster costs, HuoLala adopted Apache Tez’s Remote Shuffle Service built on Apache Uniffle, redesigning the Tez client to operate without source modifications, detailing architecture, implementation challenges, testing, stability measures, and future plans to enhance big‑data shuffle performance and cost efficiency.
The article explains how Apache Arrow’s columnar, cross‑language in‑memory format enables high‑performance, interoperable data systems—replacing traditional row‑oriented databases—by supporting dynamic schemas, zero‑copy data exchange, efficient indexing, Acero‑based query execution, and Flight/ADBC connectivity, while offering practical guidance and highlighting challenges.
Didi replaced its Elasticsearch‑based log platform with ClickHouse, redesigning architecture into isolated Log and Trace clusters, using hourly‑partitioned MergeTree tables and aggregating views to handle petabyte‑scale writes, diverse low‑latency queries, and high QPS, achieving over 400 nodes, 40 GB/s throughput, 30 % cost savings and four‑fold query latency reduction.
This article outlines the rapid growth of data in the smart automotive sector and details Changan's big data platform challenges—high cost, data accessibility, and operational complexity—and the practical migration from a Lambda to a unified Kappa architecture that delivers significant storage, compute, and maintenance efficiencies.
This article presents Shuxin Network's practical experience in building a cloud‑native, lakehouse‑integrated data platform for the financial sector, covering architecture evolution, challenges of domestic‑innovation (信创), the DataCyber solution, core components, deployment roadmap, and real‑world case studies.
This article presents NewsBreak's practical deployment of Alluxio Local Cache with Presto on S3, detailing the system architecture, cache design considerations, implementation steps, performance metrics, and future optimization directions to reduce query latency and storage costs.
This article presents OPPO's practical use of algorithms, big‑data infrastructure, intelligent compute systems, and unified modeling to improve cost efficiency and performance across its application distribution platform, while outlining future plans for edge‑cloud collaboration and large‑model deployment.
This article explains the fundamentals of Data Fabric and data virtualization, highlights the limitations of traditional centralized data warehouses, describes the three‑layer virtualization architecture, and presents a detailed securities‑industry case study that demonstrates cost, efficiency, and compliance benefits.
This article details Xiaomi’s practical deployment of Trino, covering its architectural role, core and extended capabilities, performance comparisons, integration with Iceberg and Spark, operational enhancements, multi‑cluster and ad‑hoc query scenarios, future cloud‑storage plans, and a Q&A session.
This article explains why massive news feeds need efficient deduplication, compares cosine similarity and SimHash for measuring text similarity, walks through a step‑by‑step implementation with Java code, and shows how a space‑for‑time indexing strategy can reduce duplicate‑detection complexity from O(n²) to near O(1).
Baidu’s Fusion Compute Engine, built on Spark with a one‑layer wide‑table model, combines data‑skipping, push‑down, code‑generation, vectorization and extensive tuning to cut ad‑hoc query latency to seconds, shrink storage by ~30 %, and accelerate ETL workloads while maintaining stability for massive data‑warehouse workloads.
This article analyzes Baidu's fusion compute engine for its data warehouse, detailing its architecture, optimization techniques such as data skipping, Parquet column indexing, ProjectLimit and CodeGen, and demonstrates how these innovations reduce query latency to seconds while cutting storage costs by about 30% on multi‑petabyte workloads.
This article explains why new data systems are needed, introduces Apache Arrow and its columnar in‑memory format, describes read‑time modeling and dynamic schema handling, and shows how Arrow can be used to build a complete data processing pipeline with indexing, SQL planning, and zero‑copy data exchange.
Didi leverages StarRocks materialized views with a global dictionary and bitmap aggregation to perform real‑time, high‑cardinality precise deduplication, automatically rewriting queries and refreshing views, cutting query latency by ~80%, reducing resource use ~95%, and boosting concurrent QPS up to 100‑fold, while planning further automation and bitmap optimizations.
StarRocks’ materialized view feature lets Didi replace costly fuzzy deduplication with precise, high‑concurrency deduplication for real‑time dashboards, using global dictionary mapping, layered ODS/DWD/ADS views, synchronous and asynchronous refreshes, and transparent query rewrite to cut query latency by 80% and boost QPS dramatically.
This article presents Jushuitan's cloud‑native OLAP architecture, detailing its evolution, current big‑data stack—including DataWorks, MaxCompute, Flink, Hologres, and Aerospike—along with logistics warning workflows, rule‑matching mechanisms, real‑time processing challenges, and future scalability plans.
This article introduces a new lakehouse analytics paradigm by combining StarRocks and Paimon, covering the evolution of data lake technologies, key integration scenarios, core technical mechanisms such as JNI connectors, materialized views, and future roadmap for enhanced lakehouse capabilities.
This course provides comprehensive, hands‑on training on ElasticSearch, Logstash, Kibana, and the ElasticStack ecosystem, covering advanced development, cluster design, performance tuning, security, and real‑world integration techniques for large‑scale data processing.
This article details Bilibili's tag system from its 2021 inception through successive redesigns, describing the three‑layer architecture, data flow pipelines using Hive, Iceberg, Spark and ClickHouse, crowd selection DSL, online services with Redis, performance optimizations, and upcoming governance and quality initiatives.
This article compiles a detailed list of interview question topics from leading tech firms—including search, algorithm engineering, NLP, multimodal LLMs, advertising, recommendation, risk control, and big‑data domains—covering algorithms, system design, machine‑learning concepts, and practical coding challenges.
This article demonstrates how to install the TransBigData Python package and use it for preprocessing, grid‑based aggregation, OD extraction, and both static and interactive visualizations of taxi GPS data, showcasing code examples and detailed explanations for each step.
This article details Kuaishou’s five‑year evolution of Flink, covering its background, production refactoring to Kubernetes, migration practices, and future improvements, highlighting architecture layers, resource management, observability, and testing strategies for large‑scale stream processing.
This article describes the design and implementation of a platform‑wide Data Quality Center for offline big‑data pipelines, covering research of existing solutions, design goals, system architecture based on DolphinScheduler, rule definition language, binding and execution mechanisms, and future enhancements such as lineage monitoring and real‑time checks.
This article introduces ByteDance's open‑source data integration engine BitSail, covering its background, layered architecture, recent feature enhancements, automated testing framework, CDC‑based full‑library synchronization solutions, and future development plans for connectors and real‑time data consistency.
This presentation by senior data warehouse engineer Huang Luo details Yipay's end‑to‑end data warehouse build, covering background challenges, governance framework, platform development, layered architecture, naming standards, monitoring, and future plans, offering practical insights for data engineers, architects, and business stakeholders.
This article explains how Dongchedi’s data‑warehouse team designed, implemented, and monitored a comprehensive indicator system, covering metric standards, model construction, metadata management, quality control, and diverse application scenarios to support both C‑end and B‑end business needs.
Apache Paimon provides multiple table modes—including primary key tables with fixed or dynamic buckets, Append scalable and queue tables—each with specific configurations, compaction behavior, and suitable scenarios, and the article explains their structures, performance considerations, and how to use them with Flink.
This article presents JD Logistics' one‑stop agile BI platform, detailing the complex data sources, rapid requirement changes, and Chinese‑style reporting challenges it addresses, while outlining the UData solution, product methodology, performance enhancements, and real‑world case studies that demonstrate significant efficiency gains.
This article compares ClickHouse and Elasticsearch, highlighting ClickHouse's superior write throughput, query speed, and lower server costs, then provides detailed deployment steps for ClickHouse, Zookeeper, Kafka, and FileBeat to build a cost‑effective big‑data analytics platform.
This article explores data collection governance by distinguishing compliance from reasonableness, introduces a comprehensive quality review tool system—including visual inspection, intelligent judgement, and self‑diagnosis—details key techniques such as comparison operators and sampling, and outlines a three‑layer architecture and future directions for data quality assurance.
This article provides a comprehensive overview of open‑source OLAP technologies, examines various business scenarios and data‑lake architectures, and details StarRocks' core features, performance optimizations, and future development plans within the EMR ecosystem.
This article explains how Alluxio enables lake‑warehouse integration by providing a data orchestration layer that caches data near compute, reduces storage‑compute separation costs, improves performance, and addresses challenges such as security, scalability, and multi‑cloud deployment, illustrated with several industry case studies.
This talk by Ant Group’s senior technical expert Wang Gaohang details the definition, design, mechanism, productization, and future outlook of the company’s metric system, covering concept consensus, semantic layers, workflow, AI assistance, performance optimization, and practical case studies.
This article explains Kafka as a distributed publish/subscribe messaging system, detailing its core functions, architecture, advantages, deployment methods, common use cases, and provides Java consumer and producer code examples for real‑time data processing.
Facing rapid data growth, Xingyun Retail Credit transitioned from traditional OLTP systems to an Apache Doris‑based OLAP solution, detailing the data demand generation, OLAP engine selection challenges, multi‑stage implementation, performance gains, data‑warehouse construction, and future roadmap for scalable analytics.
This article presents an in‑depth overview of ByteDance's EB‑scale HDFS, covering its new features, multi‑datacenter architecture, tiered storage implementation, data management services, capacity and fault‑tolerance strategies, as well as practical data‑protection mechanisms and related Q&A.
Gaode Technology’s 2023 roundup showcases fifteen of its most-read articles, spanning AI infrastructure evolution, cloud‑native data optimization, BEV‑based perception, real‑time crowdsourced mapping, ETA prediction, lane‑level navigation, AR HUD, architecture design, low‑code platforms, and high‑performance Android testing.
This article introduces Alluxio as an open‑source data orchestration layer, explains its architecture and core features such as unified namespace, caching strategies, and cloud‑native deployment, and shares practical experiences on using Alluxio to simplify data lakehouse construction, migration, and hot‑cold data separation in complex big‑data environments.
This article introduces the concept of database sharding, explains how sharding middleware improves scalability and performance, and reviews six popular middleware options—ShardingSphere, MyCAT, TDDL/DRDS, Atlas, Vitess, and Oceanus—helping engineers choose the right tool for billion‑row datasets.
This article introduces distributed storage concepts, outlines its five key characteristics, compares major architectures such as HDFS, Ceph, and Swift, and highlights common application scenarios like big‑data processing, cloud storage, databases, and distributed file systems.
This article details Didi's evolution from a multi‑engine OLAP stack to a unified StarRocks solution, explains the design of global dictionaries and materialized views for real‑time dashboard acceleration, and shares performance results, challenges, and future optimization directions.
Facing data security and cost challenges in SaaS, the author evaluates ClickHouse versus Elasticsearch, highlighting ClickHouse’s superior write throughput, query speed, lower storage and CPU usage, and provides detailed deployment guides for Zookeeper, Kafka, FileBeat, and ClickHouse to build a cost‑effective private analytics platform.
The article reviews how data development accelerated in 2023—with mature offline computing, rapid adoption of real‑time and lake‑warehouse solutions, and a clearer technical layering—while offering practical insights and future directions for professionals entering 2024.
iQIYI's Magic Mirror platform, evolving from 1.0 to 3.0, addresses the growing data analysis demands of the internet industry by empowering self‑service analytics, introducing multi‑stage architectures, advanced computation engines, customizable SQL, and visual dashboards, thereby improving efficiency, scalability, and data security for business users.
This article explains how Apache InLong provides automatic, secure, high‑performance real‑time data transfer to StarRocks, detailing the transactional Stream Load API, the two‑phase commit process, Flink‑based ingestion architecture, exactly‑once guarantees, and performance test results across different parallelism levels.
This article details Didi's evolution of OLAP systems, the adoption of StarRocks for high‑performance MPP analytics, and how materialized views, global dictionary mapping, and transparent acceleration were engineered to boost real‑time dashboard queries while outlining performance gains, challenges, and future optimization plans.
This article presents Didi's practical experience with StarRocks materialized views, covering the evolution of its OLAP architecture, the challenges of previous engines, the adoption of StarRocks, the design of materialized view acceleration for real‑time dashboards, and future optimization directions.
This article presents JuiceFS, a cloud‑native distributed file system that bridges the gaps between HDFS and object storage, explaining Data Lake and Lakehouse concepts, comparing storage options, detailing JuiceFS's architecture and performance benefits, and showcasing real‑world user case studies.
This article details Volcano Engine DataLeap's comprehensive data governance system for e‑commerce platforms, covering the key challenges of SLA quality, model stability, cost control, and low efficiency, and presenting a five‑part framework that includes top‑level architecture, systematic stability and cost governance, tool‑driven automation, SLA assurance processes, and future outlooks.
This article presents Jushuitan's cloud‑native OLAP architecture, covering business background, data‑warehouse evolution, real‑time processing with Flink, Hologres, and Aerospike, and detailed logistics‑warning use cases, followed by technical challenges, future outlook, and a Q&A on implementation details.
The 2024 National Advanced Computer Teaching Training held in Dongguan brought together over 200 university teachers from 119 schools to explore cutting‑edge topics such as cloud data warehouses, AI platforms, digital logic, and OpenHarmony, showcasing industry‑academic collaboration and practical hands‑on sessions.
This article reviews the evolution of data analysis and business intelligence, highlights current trends such as precision, agility, and real‑time needs, discusses common challenges, and presents the design and implementation of a unified semantic layer and indicator platform to enable agile, accurate, and real‑time analytics.
This article details Xiaomi's approach to isolating production environments for data development, covering platform evolution, security and quality challenges, physical versus logical isolation techniques, productization steps, implementation roadmap, business impact, and practical Q&A insights.
This article outlines the background, importance, scenarios, challenges, objectives, and architectural design—including RBAC and ABAC models, metadata integration, data classification, and verification mechanisms—of a comprehensive big data permission management system for secure and fine‑grained data access.
This article explains Kafka's fundamental components, version evolution, key monitoring metrics for producers, brokers, consumers and Zookeeper, and provides step‑by‑step troubleshooting methods for common issues such as slow topic throughput and message backlog.
This article presents Xiaomi's sales data warehouse practice, detailing its evolution, positioning, dimensional modeling, layered architecture, Lambda design, Iceberg integration, capability building, security governance, and future directions toward data value and real‑time metrics.
This article explains how ByteDance (Douyin) transforms its data infrastructure from isolated workshops to a unified middle platform and finally to a data flywheel, detailing the three development stages, the Data BP organizational model, real‑time analytics, A/B testing, and the resulting business benefits for large‑scale event projects.
This article presents ten practical Python code examples that demonstrate common data analysis techniques—such as handling missing values, sorting, pivot tables, visualization, association rules, outlier detection, time‑series forecasting, clustering, feature selection, and cross‑validation—to help improve business decision effectiveness.
This article explains how to build a log platform by integrating native Kibana with ClickHouse using an open‑source proxy (CKibana), covering migration motivations, architecture, configuration steps, advanced features like sampling and caching, and the resulting cost and stability benefits.
