This article presents the design, architecture, and implementation of a unified penalty data center for 58 Group, detailing the challenges of heterogeneous data sources, the selection of Flink for real‑time ETL, the use of a DSL and LRU aggregation, and the adoption of MVEL for feature recognition to achieve standardized, high‑performance penalty data processing.
This article presents JD's real-time dimension modeling case using Flink SQL, detailing two complex streaming scenarios, the difficulties of handling historical data and state management, and a component‑based solution that leverages external KV stores and optimized Flink operators to improve performance and scalability.
This article explains how Sohu Smart Media built a high‑performance tracing system for microservices by integrating Zipkin for data collection with StarRocks for storage and analytics, covering architecture, data models, SQL queries, Flink processing, and real‑world results that boost observability and engineering efficiency.
Douyu’s journey from early Spark and Storm streaming to a Kubernetes‑native Flink platform illustrates the architectural design, challenges, and solutions for large‑scale real‑time computing, data warehousing, and future scalability in a high‑traffic live‑streaming environment.
This article details how China Construction Bank's fintech subsidiary leveraged Apache Flink to ingest, join, and analyze massive front‑end, request, and response logs in real time, overcoming data silos, latency challenges, and state‑management issues to enable end‑to‑end process visibility and operational optimization.
This article presents a JD.com BI engineer's case study on applying Flink SQL to real‑time dimension modeling, detailing two complex streaming scenarios, the technical difficulties of handling historical data and performance, and a component‑based solution architecture with future roadmap considerations.
Facing inflexible point‑lookup limits and slow query times on HBase, Zepp Health redesigned its massive event‑tracking data pipeline—migrating ingestion through Kafka, Flink, and Hudi to a StarRocks‑based OLAP layer—achieving sub‑100 ms average query latency, 20 % storage savings, and dramatically faster multi‑dimensional analytics.
This talk by Tencent senior engineer Chen Liang covers Iceberg table format fundamentals, data lake ingestion, query processing, hidden partitioning, time‑travel, major features, optimization techniques such as compaction, bin‑packing, sorting and Z‑ordering, and outlines a future roadmap for improving performance and governance in big‑data environments.
The article describes a real‑time customer‑service dashboard built on Flink for streaming MySQL changes captured via Kafka, which cleans and aggregates ~60 operational metrics before writing them to ClickHouse’s MergeTree/ReplacingMergeTree tables, enabling sub‑second queries and exactly‑once guarantees while separating offline and live pipelines.
This article details AutoStream's evolution from a Java‑only Storm platform to a Flink‑based, Kubernetes‑native streaming system that integrates budgeting controls, automatic scaling, lakehouse architecture with Iceberg, and PyFlink support, highlighting the technical challenges, solutions, and future roadmap for real‑time analytics.
This tutorial demonstrates how to build a complete streaming data pipeline by configuring JDK, MySQL, Hadoop, Hive, Spark, Kafka, and Griffin, generating test data with shell scripts, processing it with Flink, and validating data quality using Apache Griffin in a Spark‑based deployment.
This article introduces Change Data Capture (CDC), compares query‑based and log‑based CDC, explains Debezium and ClickHouse, and provides step‑by‑step Flink CDC and Flink SQL CDC examples—including full Java code—to stream MySQL binlog changes into ClickHouse for real‑time analytics.
The article presents the Yunji logging system, a Flink+ClickHouse-based cloud-native platform for real-time ingestion, storage, querying, analysis, and monitoring of massive heterogeneous logs, covering its architecture, configuration center, storage design, processing flow, monitoring features, and future enhancements.
This article details China Mobile (Suzhou) Software Technology's evolution from Storm to Flink for real‑time computing, its multi‑version engine and log‑retrieval designs, signal‑business data pipeline optimizations, stability practices around ZooKeeper, and future directions in resource scaling and data‑lake integration.
This article explores the growing demand for real‑time data warehouses, compares them with traditional offline warehouses, and presents detailed architectures, layer designs, naming conventions, and case studies from companies like Didi, Kuaishou, Tencent, and Youzan, highlighting challenges, solutions, and performance optimizations.
The presentation outlines Alibaba Cloud's native data lake solution built on Apache Iceberg, covering data lake fundamentals, cloud migration challenges, Iceberg's architecture and features, real‑time ingestion with Flink, unified metadata management, security guarantees, and testing practices to ensure reliable, scalable big‑data analytics.
This article explains how Tencent leverages Apache Iceberg together with Flink to build a real‑time data lake pipeline, covering data ingestion, Iceberg's snapshot‑based read/write model, compaction and governance services, Z‑order based query optimization, performance results, and future roadmap.
This article explains how to use Async I/O in Flink, describes the ordered and unordered output modes, details the internal AsyncWaitOperator implementation with its producer‑consumer model, and discusses fault‑tolerance mechanisms including state snapshot and recovery.
This article details OPPO’s real‑time computing platform, covering its business scope, big‑data architecture built on Flink, Spark and Trino, the end‑to‑end job development lifecycle, SQL IDE features, diagnostic and monitoring mechanisms, link latency tracking, SLA guarantees, practical use cases, and upcoming lakehouse and cloud‑native evolution.
The article explains how Kafka serves as the core of a real‑time data warehouse for search, detailing its advantages over traditional databases, integration with Flink for low‑latency stream processing, architectural patterns such as Lambda/Kappa, scaling challenges, and comprehensive monitoring using Kafka Eagle.
This article explains Flink SQL’s streaming aggregation Mini‑Batch feature, covering its purpose, configuration parameters, underlying optimizer rules, operator implementations, watermark handling, buffer processing, and the optional Local‑Global two‑phase aggregation optimization for improving throughput and reducing state overhead in large‑scale data pipelines.
This article recounts ByteDance’s evolution from batch‑only Flink pipelines to a unified data‑lake integration platform, detailing the three integration modes, challenges with Spark‑based CDC, the decision to adopt Hudi over Iceberg, and how Hudi’s indexing and Merge‑On‑Read formats enable near‑real‑time analytics at massive scale.
This article introduces Change Data Capture (CDC), compares query‑based and log‑based approaches, explains Debezium and ClickHouse, and provides detailed Flink CDC and Flink SQL CDC examples—including Java source code, custom deserialization schema, ClickHouse sink implementation, and required Maven dependencies—to synchronize MySQL data into ClickHouse in real time.
This article explains the official Flink memory model diagram, shows real‑world TaskManager memory parameters, and breaks down the five major memory components—including process, Flink, JVM heap, off‑heap, Metaspace, and overhead—providing configuration guidance for optimal resource allocation.
Kuaishou dramatically increased Flink SQL adoption, introduced Group Window Aggregate and Window TVF extensions, applied aggregation state reuse and mini‑batch techniques, and enhanced stability through data‑skew mitigation and aggregate‑state compatibility, outlining future plans for streaming and batch SQL improvements.
This article presents a comprehensive design of a real-time advertising data warehouse powered by Flink, covering construction background, technical and data‑warehouse architecture, real‑time OLAP, stability and data‑quality guarantees, future plans, and the integration of Hologres for simplified processing.
This article introduces NetEase's internally incubated data lake project Arctic, explains the concept of data lakes, outlines NetEase's specific requirements for a unified streaming‑batch platform, details Arctic's core architecture, storage strategy, data‑merge mechanisms, current achievements, and future development plans.
This article explains how Kuashou tackled latency and efficiency problems in large‑scale data pipelines by adopting Apache Hudi, detailing the pain points, reasons for choosing Hudi, its architecture, model design, handling of bursty updates, back‑fill scenarios, and operational safeguards.
This article presents Meituan's real‑time data warehouse platform, describing typical streaming use cases, the evolution of its architecture from Storm and Spark Streaming to Flink, the challenges of development, operations and data quality, and the engineering solutions—including unified SQL, web IDE, UDF hosting, pipeline testing, and operator performance optimizations—implemented to support large‑scale, low‑latency analytics.
This article examines the design and implementation of a tenant‑level real‑time Customer Data Platform, detailing CDP fundamentals, business and technical challenges, key architectural components, technology selections such as graph databases, stream processing, storage engines, and the operational practices that enable high‑throughput, low‑latency data integration and analytics.
The article details the architecture, key components, testing types, and operational results of Autohome's PTS platform, which uses Docker Swarm, gRPC, JMeter, Flume‑Kafka, and Flink to conduct large‑scale distributed load testing for the 818 event and outlines future improvements toward Kubernetes and direct Kafka logging.
This article explains how to build a unified stream‑batch real‑time data warehouse using FlinkSQL, covering prerequisite knowledge, five core concepts, two implementation approaches, a comparison of traditional versus real‑time architectures, and a comprehensive hands‑on example, illustrated with diagrams.
This article explains the background, source‑code analysis, implementation details, metric granularity, and practical considerations of Flink's LatencyMarker feature for measuring full‑link job latency in streaming applications.
This article summarizes the Pravega Schema Registry project, its integration with Flink's Catalog API, the addition of Debezium CDC support, and the related implementation challenges, providing detailed DDL examples, code snippets, and architectural diagrams for building real‑time data pipelines.
This article explains the evolution, challenges, and technical solutions for building an end‑to‑end real‑time data warehouse by combining Apache Flink's stream processing with StarRocks' ultra‑fast OLAP engine, covering architecture, data models, integration methods, best‑practice cases, and future roadmap.
The talk by Tang Chuxi of Meituan explains typical real‑time data scenarios, the challenges faced when building a streaming data warehouse, and the design, development, operation, and performance‑optimisation solutions implemented on a Flink‑based platform to support massive, low‑latency business applications.
The article reviews highlights from Flink Forward 2021, describing how real‑time computing is spreading across traditional industries, the unstoppable move toward Flink SQL, the emergence of Flink ML, and the vision of a streaming warehouse built on Flink Dynamic Table technology.
This article details ByteDance's practical experience with Apache Flink, covering SQL extensions, a visual SQL platform, performance tweaks such as window mini‑batching and custom windows, join and checkpoint recovery improvements, stream‑batch integration experiments, and future roadmap plans.
This article details JD's adoption of Flink for real‑time computing, covering its evolution from Storm to Flink on Kubernetes, the platform architecture, major optimization techniques such as preview topology, backpressure handling, dynamic rebalance, checkpoint‑as‑savepoint, and outlines future plans including stream‑batch integration, stability improvements, intelligent operations, and AI integration.
SeaTunnel, the China‑originated data‑integration platform built on Spark and Flink, has been accepted into the Apache Incubator, and this article introduces its history, architecture, plugin ecosystem, deployment requirements, and numerous enterprise deployments across batch and streaming big‑data scenarios.
This article analyzes the challenges of massive data query efficiency, explains how Flink's stream processing and ClickHouse's OLAP engine complement each other, and presents a layered real‑time data‑warehouse architecture with practical guidance on data ingestion, write strategies, quality assurance, and evolving batch‑stream integration patterns.
This article explains Change Data Capture (CDC), compares query‑based and log‑based approaches, introduces Debezium and ClickHouse, and provides step‑by‑step Flink CDC and Flink SQL CDC examples—including Java source, deserialization, sink code and required Maven dependencies—to stream MySQL binlog changes into ClickHouse for real‑time analytics.
Hello Mobility unified its fragmented ElasticSearch clusters into a single, real‑time search platform—leveraging Kafka‑driven CDC, Flink stream processing, custom ES plugins, and extensive performance tuning—to deliver scalable matching, recommendation and voice services, ultimately raising completed orders by 49.8 % and driver acceptance by 37 %.
This article details OPPO’s real-time computing platform architecture, covering its background, open‑source and self‑developed components, job lifecycle, SQL IDE, diagnostic and monitoring mechanisms, SLA guarantees, practical applications such as real‑time warehousing and dashboards, and future plans for lakehouse integration and cloud‑native deployment.
Youzan upgraded its real‑time computing engine from Flink 1.10 to 1.13 to meet rising SQL and containerization demands, gaining enhanced SQL syntax, time‑function handling, Window TVF standardization, Hive integration, K8s stability, elastic scaling, richer Kafka and format support, improved metrics and debugging tools, and successfully migrated all custom connectors, UDFs, and SQL jobs to the new Kubernetes‑based platform.
This article describes Beike's real‑time computing team's end‑to‑end practices for collecting and storing Flink metrics, building visual monitoring dashboards, implementing multi‑level alerting, analyzing logs, estimating CPU and memory resources, and deploying Flink on Kubernetes with containerization and storage separation to improve stability, resource utilization, and operational efficiency.
This article details how Haro Mobility built a search‑driven ride‑matching platform using ElasticSearch and Flink, covering the business background, architectural evolution, data‑sync challenges, performance tuning, stability measures, and the resulting improvements in order completion and user engagement.
The Smart Matching Engine for Haolo’s ride‑sharing service ingests driver and passenger orders via Kafka‑Flink pipelines into Elasticsearch, then applies multi‑stage matching—nearby search, itinerary‑based filtering using ETA, angle, distance, route‑similarity and shared‑mileage calculations—and finally ranks results with evolving pre‑sorting and algorithmic models, including PMML and deep‑learning, to optimize driver‑passenger pairing.
This article details NetEase Yanxuan's real-time computing platform development from 2017 to present, covering its architecture, Flink‑SQL development environment, service‑oriented deployment, resource optimization, cloud‑native migration, comprehensive data governance, and future plans for stream‑batch integration and intelligent job diagnostics.
This article details NetEase Yanxuan's evolution of a real‑time data platform from 2017 to present, covering background, current scale, layered architecture, Flink‑SQL development IDE, service‑oriented task execution, resource‑optimizing deployment modes, cloud‑native migration, comprehensive data governance, and future batch‑stream integration plans.
This article outlines a comprehensive approach to monitoring and ensuring the accuracy and timeliness of real‑time data streams, detailing background challenges, solution design, implementation steps using Flink and automated testing, alert handling procedures, and future improvement plans.
This article presents the design and implementation of Flink task auto‑scaling, covering background, manual and automatic scaling mechanisms, architecture with RescaleCoordinator, persistence via Zookeeper and HDFS, scaling policies for parallelism, CPU and memory, and future plans for fine‑grained and time‑based resource adjustments.
This tutorial explains how to use Apache Flink 1.12 to construct a unified streaming‑batch data warehouse by integrating Hive via HiveCatalog and HiveDialect, performing read/write operations, configuring upsert‑Kafka sinks, and leveraging Flink CDC connectors for change data capture from MySQL and other sources.
This tutorial shows how to quickly construct a streaming ETL pipeline that captures changes from MySQL and PostgreSQL using Flink CDC, enriches order data with product and shipment information, and writes the results into Elasticsearch for real‑time visualization in Kibana.
This article compiles a comprehensive list of Chinese-language articles and tutorials covering big‑data technologies such as Flink, Spark, Hive, ClickHouse, data governance, and related interview preparation resources, providing a structured learning path for aspiring data engineers.
During the 2021 Double‑11 shopping festival, logistics provider DiSiFang upgraded its real‑time data warehouse with Flink and Hologres, enabling multi‑billion‑row joins, cutting costs by 50%, and delivering stable, low‑latency analytics that powered high‑frequency dashboards and improved overall delivery speed.
Autohome replaced Flink and Kylin with StarRocks to power sub‑second real‑time OLAP analytics, detailing data sources, pain points, benchmark comparisons against Apache Kylin, ClickHouse, Presto, Spark, and Doris, integration with Flink‑connector, broker‑load scripts, monitoring setup, and lessons learned from large‑scale deployments.
This article describes AutoHome's real‑time data analysis architecture, the challenges of existing OLAP solutions, the reasons for choosing StarRocks, detailed performance comparisons with Kylin, ClickHouse, Doris, Presto and Spark, and the practical integration of StarRocks with Flink, broker‑load scripts, and monitoring tools.
This comprehensive tutorial walks through configuring a Hadoop‑based environment (Flink 1.13.1, Scala 2.11, CDH 6.2.0, Hive 2.1.1, Hudi 0.10), compiling Hudi, setting up Flink and MySQL binlog, creating CDC source and Hudi sink tables, running Flink jobs, and synchronizing the results to Hive partitions for query via Hive and Presto.
This article explains how Flink’s new Sort‑Shuffle mechanism improves stability and performance for massive batch jobs by reducing file counts, optimizing I/O, minimizing memory usage, and providing detailed implementation, test results, tuning tips, and future enhancements.
This article presents a detailed learning roadmap for aspiring big‑data experts, covering foundational programming languages, data structures, Linux basics, databases, distributed system theory, and essential frameworks such as Hadoop, Spark, Flink, Kafka, and provides curated B‑site video links and reference materials.
This article explains how Flink and ClickHouse can be combined to build a real-time data warehouse with end-to-end Exactly-Once guarantees, detailing the underlying write mechanisms, transaction state machine, connector implementation, and performance test results, while also outlining future enhancements for distributed transactions.
This article provides an extensive technical guide to Apache Flink, covering its exactly‑once consumption guarantees, checkpoint and two‑phase commit mechanisms, differences from Spark, state backends, watermark handling, time semantics, window joins, CEP, backpressure, architecture layers, deployment, resource management, and common operational issues.
This guide details an end‑to‑end breast‑cancer prediction pipeline on Tencent Cloud, covering offline decision‑tree training with TI‑ONE, model packaging as a PMML service, real‑time feature generation via Oceanus and CKafka, and live inference stored in ClickHouse, all within a secure VPC.
This article details a lakehouse architecture built on Flink and Iceberg that addresses Hive‑based warehouse limitations by enabling ACID transactions, incremental snapshots, stream‑batch unification, CDC support, and various operational optimizations, ultimately achieving near real‑time data ingestion and analytics.
This article presents a technical case study of Yiche’s 818 Car Carnival data dashboard, detailing the background, business requirements, TiDB selection and architecture, encountered issues with TiDB, TiCDC and query performance, and the solutions and performance results achieved.
This article provides a comprehensive guide on optimizing Flink checkpoints, diagnosing and alleviating backpressure, and fine‑tuning memory configurations—including process, heap, off‑heap, managed, and network memory—to improve job stability and performance in large‑scale streaming applications.
This article explains how Flink's new sort‑shuffle mechanism improves large‑scale batch processing by reducing file counts, optimizing I/O, lowering memory usage, and delivering up to tenfold speedups, while also detailing configuration tips and future enhancements.
This article examines the strengths and weaknesses of Apache Iceberg, explains why Tencent selected it over alternatives, details Tencent’s own enhancements and integration with Flink, Spark, and other engines, and shares multiple real‑world implementations for building enterprise‑grade real‑time data lakes.
This article introduces Flink CDC 2.0, explains its distributed full‑load and incremental reading mechanisms, details the slice partitioning, snapshot correction, and binlog handling logic, and provides a complete Java example that demonstrates how to configure Flink SQL, MySQL source, and Kafka sink.
This article details Vipshop's OLAP evolution, covering the deployment, optimization, and containerization of Presto, ClickHouse, and Kylin, the challenges faced, self‑developed tooling, and future directions for intelligent scaling and resource management.
The article introduces Alibaba's Elastic Federated Learning Solution (EFLS), describing its business motivations, core functionalities, system architecture, sample‑set intersection, federated training pipeline, novel algorithms, product console, and future roadmap for privacy‑preserving advertising in large‑scale sparse scenarios.
This article explains Flink's state concepts, the distinction between keyed and operator state, available state backends, TTL configuration, the mechanics of checkpoints and savepoints, and the two‑phase commit protocol for ensuring exactly‑once processing in streaming applications.
This article presents Car Home's experience building a real‑time materialized view system on Apache Flink, detailing system analysis, problem decomposition, a global‑version‑based CDC algorithm, its implementation as a Flink connector, practical deployment results, and remaining challenges such as clock dependency and state size.
The article explains various dimension‑table join approaches in Apache Flink, including preloading tables into memory, using distributed cache, leveraging hot storage with async I/O, broadcasting state, and temporal table function joins, and compares their trade‑offs for different data volumes and update frequencies.
The Elastic Federated Learning Solution (EFLS) is Alibaba’s open‑source platform that enables privacy‑preserving vertical and horizontal federated learning for large‑scale sparse advertising, offering data‑intersection, high‑performance C++ training, a visual console, novel aggregation algorithms, and a roadmap toward multi‑party scaling and advanced encryption.
This article shares practical insights on designing and operating a real‑time clickstream data warehouse using Flink for streaming processing and ClickHouse for near‑real‑time OLAP, covering dimensional modeling, layered architecture, Flink‑ClickHouse sink implementation, and data rebalancing strategies.
This article explains how Kuaishou tackled late data scheduling, costly synchronization, and inefficient back‑fills by adopting Apache Hudi, detailing the pain points, reasons for choosing Hudi, and step‑by‑step implementation to achieve fast, fresh, and scalable data processing.
This guide shows how to build a real‑time UV and PV analytics pipeline on Tencent Cloud Oceanus by connecting a self‑hosted Kafka cluster to Flink SQL, using Redis for deduplicated visitor counts, page view logs, and conversion‑rate calculations via hop windows.
This article explains how Flink SQL implements Top‑N queries, shows the standard SQL syntax, analyzes the logical and physical execution plans generated by the optimizer, and details the internal Rank node, optimization rules, state handling, and configuration options for efficient stream processing.
The article describes how Yixin Group’s product team created an in‑house intelligent data warehouse using Hadoop, Flink/Spark, and standardized data services to transform scattered automotive‑finance data into a secure, scalable platform that supports real‑time analytics and drives business growth.
This article explores the evolution of data lakes, compares major cloud providers' lake architectures, introduces the emerging lakehouse concept, and provides a step‑by‑step Flink‑Iceberg implementation—including dependencies, catalog setup, table creation, checkpointing, and Kafka ingestion—demonstrating practical big‑data streaming solutions.
This article details Bilibili's comprehensive optimization of Flink real‑time computing, covering connector stability improvements, SQL interval‑join enhancements, runtime state and checkpoint refinements, a diagnostic tool, and future directions for high‑throughput streaming workloads.
This article explains how to monitor Flink jobs running on YARN by leveraging Flink metrics, configuring reporters, defining custom metrics, and visualizing the data in real time with Prometheus, Grafana, and Graphite‑exporter, complete with deployment diagrams and code examples.
The interview with Kuaishou senior architect Zhao Jianbo details the three‑phase evolution of its trillion‑scale big data platform, covering foundational Hadoop services, real‑time and OLAP extensions, deep customizations, Spring Festival Gala challenges, scheduling innovations, Hadoop usage, and the relationship between big data and cloud architectures.
This article details NetEase Games’ evolution of its Flink SQL platform, from the early StreamflySQL v1 template‑JAR approach to the v2 SQL‑Gateway architecture, discussing design decisions, challenges such as metadata persistence, multi‑tenant security, horizontal scaling, and job state management.
This article details the design and implementation of 58.com’s real‑time computing platform, covering its architecture, data ingestion, storage, Flink‑based stream processing, SQL extensions, performance optimizations, Storm‑to‑Flink migration tools, the Wstream management console, state handling, monitoring, and future roadmap.
This article explains how bytecode and code‑generation improve Spark SQL performance, compares Java I/O and MapReduce InputFormats, reviews serialization choices in Spark and Flink, and describes reflection‑based DataFrame creation, storage‑memory eviction, fail‑fast design, and ConcurrentHashMap usage in big‑data frameworks.
This article provides a comprehensive guide to Flink Table and SQL window semantics—including group, tumbling, sliding, and session windows—covers over windows, demonstrates how to define windows in SQL, explains built‑in functions, shows how to implement scalar, table, aggregate and table‑aggregate UDFs, and details Flink's integration with Hive, complete with Maven dependencies and runnable examples.
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 provides a comprehensive guide to Apache Flink's Table API and SQL, covering required dependencies, the differences between old and Blink planners, program structure, table environment creation, catalog registration, query execution, conversion between DataStream and Table, update modes, and time attribute handling, with Scala code examples throughout.
This article explains Flink's memory architecture, covering on‑heap and off‑heap memory concepts, garbage collection, allocation strategies, memory segments, buffers, the memory manager, and how network transmission and back‑pressure are handled to achieve efficient streaming processing.
This article explains Flink's various join mechanisms—including interval‑based joins, window‑based joins, streaming SQL joins, and dimension‑table joins such as preload, hot‑storage, broadcast, and temporal‑table function—provides detailed code examples in Java, discusses state management and performance considerations, and summarizes the four main dimension‑table join patterns.
This article explains the concepts and background of network flow control, compares static rate limiting with dynamic feedback backpressure, describes TCP's sliding‑window mechanism, and details how Flink implements both TCP‑based and credit‑based backpressure to handle mismatched upstream‑downstream speeds in streaming applications.
This article reviews the evolution of big data technologies in the last five years, covering streaming and batch processing frameworks, column‑store NoSQL databases, programming language trends, the cloud‑native multi‑model database Lindorm, and practical Flink/Blink usage with code examples.
This article, compiled by community volunteers, examines various CDC data real‑time read/write solutions for data lake architectures, comparing offline HBase, Apache Kudu, Hive, Spark + Delta, and ultimately advocating Flink + Iceberg for efficient, correct, and scalable streaming ingestion and analytics.
Meituan Waimai’s data intelligence team outlines a universal real‑time data‑warehouse methodology that combines a production platform with an interactive analytics engine, detailing scenarios, technology choices, architectural designs, platformization, SLA management, and a practical Lambda‑style case study.
This article provides an in-depth overview of data lake concepts, definitions, and essential features, followed by detailed case studies of enterprise data lake implementations and comparative analysis of leading data lake table formats—Iceberg, Hudi, and Delta Lake—highlighting their architectures, capabilities, and trade‑offs.
This article explains Flink’s three‑layer architecture (APIs & Libraries, Core, Deploy), details its programming interfaces, runtime engine, deployment options, and core concepts such as stateful computation and time semantics, providing a comprehensive guide for building robust stream and batch applications.
This article introduces a universal method for building a real-time data warehouse at Meituan Waimai, covering streaming technologies, architecture choices such as Lambda and Kappa, component design, feature production, SLA management, and practical OLAP solutions using Flink, Storm, and Doris.
