Flink 1.13 introduces passive scaling that lets users adjust parallelism to resize jobs, adds visual tools such as load/back‑pressure charts, CPU flame graphs, and state‑backend metrics for deeper performance insight, and includes numerous community optimizations for easier upgrades and operation.
This article presents a comprehensive overview of a Flink-powered real-time recommendation system, detailing its v2.0 architecture, module functions, recommendation algorithms, front‑end and back‑end interfaces, Docker‑based deployment of MySQL, Redis, HBase, Kafka, and step‑by‑step startup procedures.
Beike's real-time computing platform, led by Liu Liyun, has evolved from early Spark Streaming to a Flink-based system with SQL 1.0, 2.0, and upcoming 3.0, supporting a large-scale data warehouse, event-driven processing, extensive monitoring, and diverse business scenarios across the company's operations.
This article presents a technical deep‑dive into Bilibili’s evolution from offline to real‑time data processing, describing the challenges of timeliness, ETL, AI feature engineering, and the design of a Flink‑on‑YARN incremental pipeline that supports trillion‑scale message throughput and AI‑driven real‑time applications.
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 provides a step‑by‑step guide to build a data pipeline that captures MySQL binlog, streams it through Canal into Kafka, processes it with Flink, and finally writes the results into TiDB, covering environment setup, component deployment, configuration, and verification.
This article details Baixin Bank's construction of a Flink‑driven real‑time computing platform integrated with Hudi as a real‑time data lake, covering background, architecture, data collection, transformation, storage layers, technical challenges, future roadmap, and practical lessons for similar big‑data initiatives.
This article explains how Flink on YARN allocates TaskManager memory, breaks down the JVM heap, network buffers, and Flink Managed Memory, and shows how to calculate each component using configuration parameters and source‑code analysis.
This article presents the background, requirements, architectural design, component interaction, and implementation details of AutoHome's real‑time data transmission platform built on Apache Flink, highlighting its high availability, exactly‑once semantics, scalability, DDL handling, and integration with existing streaming services.
This article presents Kuaishou's comprehensive engineering practices for improving Flink's stability, task startup latency, and SQL performance, including high‑availability Kafka connectors, fault‑recovery mechanisms, I/O reductions, asynchronous job upgrades, aggregation optimizations, and future resource‑utilization plans.
This article describes how Linkflow migrated mutable customer data from MySQL to an Apache Hudi data lake using Debezium‑in‑Flink CDC, addressing challenges such as snapshot resumability, partial updates, row‑key merging, schema evolution, indexing, and concurrent writes to achieve minute‑level data freshness and improved offline processing performance.
This article walks through downloading the required Flink and Hudi components, building Hudi for Scala 2.12, and demonstrates step‑by‑step how to create, populate, query, and update Hudi tables in both batch and streaming modes using Flink SQL, complete with code snippets and result screenshots.
This article details Zhihu’s transition from a Sqoop‑driven data integration system to a Flink‑centric platform, covering business scenarios, historical architecture, design goals, technology choices, performance optimizations, and future plans for unified streaming‑batch processing across diverse storage systems.
This article demonstrates how to configure Flink 1.12 to read data from HBase, covering the required environment components, HBase table creation, Maven dependencies, Java POJO and Flink‑SQL code, and showing the query results with and without printing the TableResult.
This article shares ten practical pitfalls encountered when moving hourly Spark session jobs to Flink, covering parallelism load imbalance, state TTL, checkpointing strategies, logging, JMX debugging, state migration risks, reduce vs process choices, input data validation, event‑time handling, and external storage considerations, along with concrete configuration snippets and performance tips.
This article describes a real-time log processing platform that integrates Kafka, Flink, Drools rule engine, Redis, and Elasticsearch to unify heterogeneous log formats, extract business metrics, and provide configurable, dynamic data processing for large‑scale logging scenarios.
This article details a financial company's exploration of Apache Flink for real‑time processing, covering its unique business constraints, end‑to‑end data pipeline, single‑table and multi‑table use cases, implementation challenges, code snippets, data initialization, testing strategies, and lessons learned.
This article explains how to collect, process, and visualize Flink job logs on Kubernetes using an EFK stack with Fluentd, covering logging architectures, deployment of Elasticsearch, Kibana, and Fluentd, and the backend logic for querying and displaying logs in a feature platform.
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 explains how to deploy a PyFlink feature platform on Kubernetes, covering basic K8s concepts, Flink execution graphs, various deployment modes, preparation steps, detailed Standalone and Native deployment procedures, and practical tips for efficient big‑data processing.
This article explores the evolution of real-time computing in the big data domain, detailing Apache Flink's capabilities, architectural designs, technology selections such as Kafka, Canal, HBase, ClickHouse, and provides practical implementation guides and case studies from Alibaba, Tencent, and other enterprises.
This article presents the evolution, architecture, and key innovations of Bigo's real‑time computing platform—covering its migration from Spark Streaming to Flink, unified platform design, development tools, operational enhancements, and the efficiency gains achieved in business scenarios such as ETL and AB‑testing.
The article reviews the current state of offline Hive‑based data warehouses, explains the emergence of real‑time data warehouses (1.0) built on Kafka and Flink, discusses their limitations, and outlines the progression toward batch‑stream unified architectures (2.0 and 3.0) leveraging data‑lake technologies such as Iceberg.
This guide details comprehensive Flink performance tuning techniques, covering memory configuration, GC settings, parallelism adjustments, process parameters, partitioning strategies, Netty network tuning, checkpoint optimization, and common issues such as data skew and resource bottlenecks.
This article describes Tencent Games' end‑to‑end real‑time data platform, covering its construction background, the unified OneData development framework, the OneFun data‑service API layer, micro‑service and ServiceMesh management, and the operational benefits achieved through automation, standardization, and scalability.
This article explains how Flink's DataStream API uses AggregateFunction and session windows, details the MergingWindowAssigner and MergingWindowSet implementations, and demonstrates timer registration and processing with KeyedProcessFunction, providing full code examples and internal workflow analysis.
The article describes how algorithm testing teams tackled data‑quality and interface‑semantic monitoring problems by building a unified business monitoring platform that checks table, storage and service consistency, validates response semantics, and, through dashboards, alerts and correction tools, quickly identified dozens of offline and online issues, guiding future reliability enhancements.
iQIYI transformed its data infrastructure from a traditional offline T+1 model to a comprehensive real‑time ecosystem—leveraging Kafka, Flink, a three‑layer Stream Data Service Platform, the Talos drag‑and‑drop pipeline, and a Druid‑based analytics platform—to enable low‑latency monitoring, personalized recommendations, ad targeting, and continuous machine‑learning workflows while planning future stream‑batch integration and lake‑warehouse convergence.
This article presents a comprehensive overview of data lake implementations, detailing Huawei's production‑scene platform, a real‑time financial data lake architecture using Kafka, Flink and Iceberg, and Soul's Delta Lake practice with Spark, Hive, and custom ETL tools, highlighting design choices, processing flows, and operational considerations.
This article presents ByteDance's recent Flink enhancements, detailing a single‑point recovery mechanism for the network layer and a regional checkpoint strategy that together improve failover latency, reduce output loss, and enable scalable, high‑throughput stream processing for large‑scale real‑time recommendation workloads.
This article details common Flink streaming problems such as data skew causing task back‑pressure, oversized Kafka messages, high‑throughput ack settings, slot removal errors, checkpoint timeouts, and resource constraints, and provides concrete configuration changes and architectural adjustments to resolve them.
This article explains the upsert‑kafka connector in Flink, its configuration parameters, step‑by‑step usage with SQL examples, and demonstrates a complete pipeline that reads Kafka streams, aggregates page view metrics, and synchronizes the results to TiDB in real time.
This article shares ten practical pitfalls encountered while moving hour‑level Spark session processing jobs to Apache Flink, covering parallelism skew, state TTL, checkpoint handling, logging, debugging, state migration, Reduce vs Process, input validation, event‑time handling, and the trade‑offs of storing data inside Flink.
The article provides a comprehensive technical guide on Flink's FileSystem, JDBC, and Kafka connectors, detailing their source and sink implementations, core code logic, checkpoint handling, partition commit strategies, and complete SQL usage examples for streaming applications.
This article explains how Flink SQL can be used to construct a unified stream‑batch ETL pipeline for data warehouses and data lakes, covering data integration, CDC support, streaming writes to Hive and Iceberg, and various join techniques such as regular, interval, and temporal joins.
This article explains how to boost the efficiency of massive regular‑expression matching by using Intel's Hyperscan library, integrating it with Apache Flink for streaming processing, and providing deployment guidelines for both private and internal environments.
The article describes JD's end‑to‑end online learning pipeline for retail search, covering the background, system architecture, real‑time feature collection, sample stitching, Flink‑based incremental training, parameter updates, and full‑link monitoring to achieve low‑latency, high‑accuracy model serving.
This article explains how to boost massive regular‑expression matching speed by using Intel's Hyperscan engine together with Apache Flink for streaming, covering security scenarios, architectural challenges, deployment options, usage examples, performance results, and future enhancements.
This article explains how NetEase games collect heterogeneous logs, design a Flink‑driven streaming ETL pipeline, handle schema‑free sources, implement Python UDFs with Jython, optimize HDFS writes, manage real‑time and offline warehouses, and share practical tuning and fault‑tolerance techniques.
This article explores practical methods for optimizing Flink real‑time task resources on Kubernetes, focusing on memory usage analysis via GC logs and message‑processing capacity assessment, proposing automated detection of over‑provisioned memory and CPU, and outlining a workflow for resource adjustment to reduce costs.
This article details JD Search's journey from a Storm‑based pipeline to a Flink‑driven architecture backed by Apache Doris, covering business requirements, technical challenges, design trade‑offs, performance optimizations for massive traffic spikes, and future plans for their real‑time OLAP data warehouse.
The article details Apache Iceberg 0.11.0's core enhancements—including partition changes, SortOrder, extensive Flink and Spark integrations, CDC/Upsert support, hash‑based write distribution to reduce small files, and upcoming 0.12.0 roadmap—while providing practical SQL and API examples for data‑lake practitioners.
This article presents Meituan's use of Flink to enable incremental data warehouse production, covering the warehouse architecture, streaming data integration evolution, real-time OLAP applications, platform design, and future directions for unified stream‑batch processing.
This guide walks through setting up Apache Zeppelin as a low‑cost, SQL‑centric development platform for Flink, covering environment preparation, installation, interpreter configuration, execution modes, verification, common pitfalls, dimension‑table joins, custom UDFs, Redis integration, and dual‑stream join techniques.
This article, presented by Tencent senior engineer Du Li, details the current state of Flink SQL, compares Jar, Canvas, and SQL modes, introduces window‑function extensions, retract‑stream optimizations, and outlines future roadmap plans for cost‑based optimization and new features in the real‑time computing platform.
This article provides a comprehensive overview of Apache Iceberg, covering its origins, key features, practical Spark and Flink code examples, notable deployments at Alibaba and Tencent, and its future role as a universal table format for big‑data analytics.
The article reviews Flink 1.12's major enhancements, including comprehensive SQL capabilities, deep integration with Hive via catalog and streaming support, and a practical code example that demonstrates how to write streaming data into Hive tables while handling partition commits and small‑file merging.
This article, presented by senior data platform engineer Mao Xiangyi of TAL Education, details the design and implementation of the company’s real‑time T‑Streaming platform, covering its three‑layer data architecture, batch‑stream integration techniques, ODS layer real‑timeization, Flink SQL development workflow, hybrid‑cloud deployment, and a case study of K‑12 renewal reporting.
This talk by Ba Xueyu, a senior big data platform engineer at Zhongyuan Bank, outlines the background, architecture, and engineering practices of a real‑time financial data lake, highlighting its open, timely, and integrated design, streaming platform implementation, and use cases such as anti‑fraud and real‑time BI.
Facing a massive 1500‑node Flink 1.4.2 cluster handling over 12,000 tasks and 30 trillion daily events, we migrated to Flink 1.10, detailing new DDL/Catalog support, SQL enhancements, memory tuning, compatibility patches, extensive testing, and engine optimizations such as task‑load metrics and balanced sub‑task scheduling.
This article explains how to diagnose and optimize Flink jobs by first examining metrics, then checking resource allocation, analyzing throughput and back‑pressure, and finally tuning JVM settings, while providing concrete configuration examples and practical tips for big‑data practitioners.
This article explains how to use the Cloud Native Prometheus ecosystem—including Prometheus Server, exporters, Pushgateway, Alertmanager and Grafana—to collect, store, query and visualize Flink job metrics, providing a complete monitoring solution for production clusters.
This article presents a detailed case study of NetEase Cloud Music’s real‑time analytics platform built on Kafka and Flink, covering background, architectural choices, platform‑level design, operational challenges, solutions such as the Magina framework, and a Q&A on reliability and monitoring.
At Youzan, Flink real‑time tasks running on Kubernetes are optimized by daily GC‑log memory analysis and Kafka‑throughput monitoring, which compute recommended heap sizes and parallelism adjustments to eliminate over‑provisioned CPU and memory, automate alerts, and pave the way for fully automated resource tuning.
DiDi upgraded its massive real‑time computing engine from Flink 1.4.2 to Flink 1.10, implementing a transparent migration across 1500 machines, adding native DDL, binary rows, MiniBatch, improved scheduling and window functions, and establishing a rigorous testing pipeline that achieved 99.9 % compatibility while preventing OOM issues.
This article recounts the author’s journey building a real‑time data warehouse using Flink, Kafka, Redis, and ClickHouse, describing the initial batch‑oriented setup, successive architectural evolutions, challenges with wide tables and dimension data, and the final OLAP‑centric solution with secondary caching.
This guide explains how to integrate Flink 1.12 with Hive using HiveCatalog, covering required dependencies, Blink planner configuration, SQL dialect switching, Hive UDF support, temporal table joins, and provides complete code snippets for a streaming‑batch unified data warehouse solution.
This article presents a detailed collection of 2021 Flink interview questions covering checkpoint mechanisms, watermarks, state backends, join types, fault tolerance, resource configuration, and recent Flink 1.10 features, providing concise explanations and code examples for each topic.
This article describes how NetEase built a real‑time log intelligent classification system using Flink and AI algorithms, detailing the challenges of massive log volumes, the Drain template‑extraction method, algorithm workflow, performance results, and a practical case study that demonstrates reduced alert storms and faster issue diagnosis.
This article describes how a fast‑growing company built a layered real‑time data warehouse on Flink, detailing the evolution from a simple 1.0 pipeline to a 2.0 architecture with ODS, DWD and ADS layers, dimension joins, exactly‑once sinks, HDFS partitioning, monitoring, and future improvements.
This article provides a step‑by‑step guide on configuring MySQL binlog, installing and deploying Oracle GoldenGate, extracting changes, converting them to JSON, and streaming the data into Kafka for real‑time processing, complete with code snippets and verification procedures.
This article provides a step‑by‑step guide on configuring the MySQL CDC connector for Flink, covering Maven and SQL client dependencies, MySQL user setup, connector options, table creation via SQL and Stream API, key features, common issues, and practical troubleshooting tips.
This article examines the pervasive small‑file issue in big‑data environments, explains its impact on storage and processing performance, and presents a comprehensive set of solutions—including file merging, Hadoop archives, SequenceFiles, HBase, CombineFileInputFormat, and Spark/Flink strategies—to mitigate metadata overhead and improve I/O efficiency.
This article details how to migrate Spark Streaming jobs to Flink Streaming by creating custom SourceFunction and SinkFunction implementations, including a RocketMQ source connector and an HBase sink, with code examples, configuration tips, and discussion of checkpointing and watermark handling.
At Youzan’s Big Data Technology Salon, over 100 attendees heard leaders from Youzan, NetEase Yishu, and Didi discuss cost governance, Apache Iceberg data lakes, large‑scale Flink real‑time computing, and data‑driven growth strategies, highlighting practical implementations, savings of millions and tools for merchant empowerment.
This tutorial introduces Apache Zeppelin, explains REPL and Jupyter concepts, outlines its core features and project structure, and provides step‑by‑step instructions for installing Zeppelin, creating notebooks, and connecting to databases, Spark, and Flink with practical code examples.
This article explains how to leverage Flinkx and Flink Stream API to create a unified data‑sync task that extracts data from sharded MySQL tables, splits the workload, and pushes it to an MQ cluster, while detailing the underlying InputFormat and Reader architecture.
This article provides a comprehensive overview of data middle platform concepts, covering data aggregation, collection tools, development modules, job scheduling, baseline control, heterogeneous storage, permission management, real‑time and offline processing, governance, services, and implementation details for building robust big‑data solutions.
This article explains why Kerberos is needed for Hadoop clusters, details the Kerberos authentication workflow, and provides step‑by‑step instructions for configuring Flink to run on a Kerberos‑protected YARN environment using delegation tokens or keytab files, along with proxy‑user settings.
This article explains how to build a real‑time data processing platform using Flink, covering the Lambda architecture, design approaches, SQL and custom‑Jar task definitions, UI drag‑and‑drop, cluster resource management on Yarn and Kubernetes, submission modes, scheduling, permission and metadata handling, logging, and monitoring with Prometheus and Grafana.
This article explains Flink’s architecture and details how to run Flink on Kubernetes using both Standalone and native modes, covering Session and Per‑Job deployment, required ConfigMaps, Deployments, Services, and command‑line steps for creating, submitting, and deleting Flink jobs.
The Youzan Big Data Technology Salon brought together Youzan, NetEase and Didi to share practical approaches for cutting data‑infrastructure costs, building an Apache Iceberg‑based data lake, scaling Flink real‑time workloads, and creating a data‑driven growth platform that leverages tracking, A/B testing and analytics.
This article details Jingdong's evolution from Storm to Flink, the architecture of its Kubernetes‑based real‑time computing platform, extensive containerization practices, performance and stability optimizations, and the future plan to unify batch‑stream processing while expanding SQL support and intelligent operations.
This article explains how Flink enables end‑to‑end machine‑learning workflows through AI Flow, covering the background of Lambda architecture, AI task stages, the advantages of Flink, AI Flow components, AI Graph concepts, integration with Python and TensorFlow, and a real‑world advertising recommendation use case.
This article explains how Apache Flink integrates with data lake architectures, especially using Apache Iceberg as a table format, to enable real‑time streaming ingestion, CDC processing, near‑real‑time lambda architectures, and future enhancements like automatic file merging and row‑level deletes.
This article explains how Flink enables end‑to‑end AI workflows through the AI Flow platform, covering the Lambda architecture background, AI task pipeline stages, the reasons for choosing Flink, AI Flow’s graph model, core services, integration with ML pipelines, and real‑world advertising recommendation use cases.
Alibaba’s Customer Experience (CCO) team transformed its real‑time data platform by evolving from a Lambda‑style database architecture to a cloud‑native real‑time data warehouse powered by Hologres and Flink, achieving higher throughput, lower latency, reduced costs, and self‑service analytics for massive Double‑11 traffic.
This article explains Change Data Capture (CDC) and how Flink CDC can be used for incremental data synchronization, real‑time materialized views, audit logging, and CDC‑based joins, providing code examples, Maven dependencies, and SQL/Java snippets for MySQL and Kafka integrations.
Alibaba’s 2020 Double‑11 achieved unprecedented real‑time processing of 4 billion records per second and 7 TB of data per second using Flink, showcasing the stability, performance and efficiency of its stream‑batch unified architecture across diverse business scenarios.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, explains its core algorithms, performance comparison with Spark ML, version‑wise feature evolution, and provides practical quick‑start instructions for both Java (Maven) and Python (PyAlink) users, including data source handling, type conversion components, unified file‑system operations, and an overview of its FM algorithm implementation.
This article details the three‑stage evolution of 58.com’s commercial data warehouse, describing its massive scale, four‑layer architecture, technical challenges, migrations from MapReduce to Hive and Flink, real‑time streaming upgrades, and the resulting improvements in stability, accuracy, and timeliness.
This article walks through the evolution of big‑data systems to modern stream processing, explains core Flink concepts such as state, checkpoints, event‑time and windowing, and details Kuaishou’s real‑time UV calculation and fast‑failover techniques for high‑availability streaming jobs.
This article explains the Flink operator chaining mechanism, detailing how logical plans are transformed into JobGraph and ExecutionGraph, the conditions for chaining, code implementations, and how the runtime constructs OperatorChain to improve execution efficiency.
This article explains the difference between APIs and Java SPI, describes how SPI enables plug‑in development through ServiceLoader, and illustrates its practical use with JDBC driver loading and Flink table factories, providing code snippets and implementation steps for backend developers.
This article explains how to build a real‑time click‑stream data warehouse using Flink for stream processing and ClickHouse for near‑real‑time OLAP, covering click‑stream characteristics, dimensional modeling, layered warehouse design, async dimension joins, sink implementation, and data rebalancing strategies.
This article details the high‑complexity logistics scenario of Cainiao's international import business, explains the evolution from offline to real‑time data warehouses (versions 1.0 and 2.0), describes the layered architecture, enumerates technical challenges such as multi‑source joins, state explosion, out‑of‑order processing, and presents concrete solutions using Flink features, logical middle‑layers, union‑all joins, deduplication, timer services, and batch‑stream hybrid processing.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, detailing its core algorithms, performance advantages over Spark ML, version evolution, Maven and PyAlink installation steps, data‑source integrations, FM algorithm support, and unified file‑system operations for both batch and streaming workloads.
The article describes a real‑time video recommendation indexing system that replaces 30‑minute batch builds with an Elasticsearch‑based service, integrates prior and posterior data pipelines, ensures consistency via locking and version checks, enables zero‑downtime upgrades, smooths write spikes, and boosts recall performance through multi‑level caching and ES tuning, delivering sub‑40 ms latency and significant business growth.
This article introduces the Actor model, its fundamental rules, and how it underpins Flink and Spark RPC mechanisms, then explains the Akka framework, its actor hierarchy, supervision, lifecycle, and dispatcher, providing a concise foundation for distributed big‑data processing.
Flink SQL, the high‑level SQL interface for Apache Flink, offers language‑independent, dependency‑free, easy‑to‑use stream processing with advanced features such as DDL, UDFs, time semantics, windowing, pattern matching, and built‑in connectors, supporting data synchronization, batch‑stream fusion, Hive integration, and various product enhancements.
Gaode’s TestPG platform solves full‑link load‑testing bottlenecks by unifying traffic capture with Iflow, converting logs into standardized corpora via a Flink pipeline, and applying corpus‑intelligence that extracts seasonal feature statistics and predicts distributions for precise, feature‑level throttling, enabling faster, more reliable testing and future autonomous optimization.
This guide explains how to set up Apache Zeppelin as an interactive notebook for Flink SQL, covering download, environment configuration, Zeppelin and Flink interpreter settings on YARN, Hive integration, and step‑by‑step testing of streaming SQL queries.
The article explains how Flink 1.11 deepens its integration with Hive, covering background, new connector features, simplified dependency management, enhanced Hive dialect, streaming writes and reads, temporal table joins, and how these capabilities enable a unified batch‑streaming data warehouse.
This article presents NetEase Cloud Music's real‑time computing platform built on Flink and Kafka, covering background, architectural design, Kafka and Flink selection reasons, platformization, warehouse usage, encountered challenges, and the solutions implemented to improve reliability and performance.
This article details Didi’s end‑to‑end construction of a real‑time data warehouse for the Ride‑Sharing (顺风车) business, covering motivations, layer‑by‑layer architecture, naming conventions, StreamSQL capabilities, operational tooling, achieved results, challenges, and future batch‑stream integration plans.
Lyft transformed its legacy data pipeline by designing a cloud‑native, Flink‑based near real‑time analytics platform that ingests billions of events, writes Parquet files to S3, leverages Presto for interactive queries, and implements multi‑stage non‑blocking ETL, fault‑tolerant back‑fill, and extensive performance optimizations.
This article provides a comprehensive overview of modern real‑time big‑data solutions, detailing Spark Structured Streaming capabilities, CarbonData’s storage architecture, Meituan’s Flink deployments, and Huawei Cloud Stream’s unified streaming service, highlighting their features, challenges, and future directions.
This article details NetEase Cloud Music's evolution of a real‑time data warehouse built on Flink 1.9 and Calcite, covering platform scale, architectural design, metadata management, SDK simplifications, monitoring improvements, and concrete use cases such as AB‑testing, live reporting, and feature serving.
This article explains how VIPKID leverages real‑time streaming with Flink to build a unified data platform, automatically tag and process help requests during 1‑v‑1 live classes, and achieve significant reductions in manual monitoring while improving course quality and user experience.
This article explains why real‑time data warehouses are needed, outlines their core principles, compares them with offline warehouses, describes typical use cases such as real‑time OLAP, dashboards, feature generation and monitoring, and provides a step‑by‑step guide to designing, implementing, and operating a Flink‑based streaming warehouse with Kafka, HBase, and metadata management.
