KaptureCX migrated its core analytics from ClickHouse to StarRocks, introduced RisingWave and Kafka for CDC, and achieved millisecond‑level query latency, a reporting cycle cut from weeks to one day, and a solid data foundation for AI‑driven services.
This article explains how to extend a Flink CDC job that already syncs an entire MySQL database to Doris so that newly created tables are automatically created in Doris in real time, using the CdcTools utility, side‑output streams, and asynchronous I/O.
MySQL 9.6 shifts foreign‑key checks and cascade handling from the InnoDB storage engine to the SQL engine, eliminating hidden changes, improving binary‑log visibility, and delivering full‑log replication and analytics without sacrificing performance.
This article explains how to use Flink CDC together with the CdcTools utility to automatically capture newly created MySQL tables and synchronize both their schema and data to a Doris database in real time, covering the required code, side‑output handling, async execution, and a special delete‑sign field.
An e‑commerce junior developer tackles the challenge of regularly syncing order data to a data warehouse, evolving from naïve full‑table copies to incremental sync, batch processing, cursor‑based pagination, performance tuning, and finally a real‑time CDC‑plus‑message‑queue architecture, while addressing reliability, ordering, and scaling issues.
Ctrip transformed its traditional T+1 offline warehouse into a near‑real‑time lakehouse by integrating Flink CDC with Apache Paimon, designing a two‑stage CDC ingestion, optimizing performance, implementing dynamic updates, and deploying the solution across multiple business scenarios, achieving minute‑level latency, reduced costs, and faster data‑driven decisions.
This guide walks a new e‑commerce developer through the evolution of order data synchronization—from naïve full‑table loads, through incremental and batch strategies, cursor‑based pagination, performance tuning, and finally to real‑time CDC with message queues—highlighting pitfalls and practical solutions.
Action OMS, a customized version of OceanBase’s OMS tool, enables seamless migration and real‑time data subscription from Microsoft SQL Server (2008‑2019 Enterprise) to OceanBase, detailing preparation, configuration, CDC setup, and best‑practice considerations for reliable, low‑latency data transfer.
Suixingfu rebuilt its payment data pipeline by replacing a fragmented Lambda stack with a unified Porter CDC + StarRocks + Elasticsearch architecture, achieving three‑fold query speed, ten‑fold analytics efficiency, 20% storage reduction, and sub‑second data‑capture latency across high‑concurrency, ad‑hoc, and batch workloads.
This article explains the challenges of maintaining data consistency in distributed, event‑driven microservice architectures and introduces three practical patterns—Outbox, Original Event Handling, and Self‑Read—to guarantee reliable state synchronization across services, even when failures occur.
This article provides a comprehensive overview of Flink CDC 3.3, detailing its CDC fundamentals, new connectors, Transform module enhancements, asynchronous snapshot splitting, community adoption, and upcoming roadmap for broader ecosystem support and batch‑mode execution.
This article presents a comprehensive overview of Flink CDC + Hologres high‑performance data synchronization, detailing write and consumption optimizations, architectural principles, and future directions to achieve low latency and high throughput in real‑time data pipelines.
This article explains CDC fundamentals, details Flink CDC's architecture and advantages, provides setup steps, code examples for SQL and DataStream APIs, discusses performance tuning, consistency, common issues, and typical real‑time data integration scenarios.
Apache SeaTunnel is an open‑source, distributed data integration platform that enables efficient real‑time data synchronization across diverse sources and destinations, supporting both streaming and batch processing, with detailed architecture, connector plugins, CDC handling, transform capabilities, and deployment strategies for large‑scale data pipelines.
This article explores four practical methods for synchronizing MySQL data to Elasticsearch—including synchronous and asynchronous double writes, SQL extraction, and binlog real‑time replication—while reviewing popular migration tools such as Canal, Alibaba DTS, and Databus to help you choose the right solution.
This article summarizes an advanced Flink CDC presentation, covering Flink CDC fundamentals, real‑time Flink integration, CDC‑YAML core capabilities, supported sync links, Transform and Route modules, monitoring metrics, schema‑change strategies, typical use cases, performance optimizations, demo implementations, and future development plans.
This article introduces Flink CDC YAML, outlines its core capabilities and application scenarios, compares it with SQL and DataStream jobs, showcases enterprise‑grade features of Alibaba Cloud Flink CDC, and provides a step‑by‑step tutorial to build a complete CDC YAML job in just ten minutes.
Tencent's advertising team replaced a traditional HDFS‑Hive warehouse with an Apache Iceberg‑based data lake, adding primary‑key tables, multi‑stream merging, adaptive compaction, and Spark SPJ optimizations to achieve minute‑level feature update latency, 10× back‑fill speed, and up to 60% storage savings.
This guide compares four MySQL‑to‑Elasticsearch synchronization methods—synchronous dual‑write, asynchronous MQ‑based dual‑write, timer‑driven SQL extraction, and real‑time Binlog replication—and reviews popular CDC tools such as Canal, Alibaba Cloud DTS, Databus, and others to help you choose the right solution.
This article reviews four common MySQL‑to‑Elasticsearch synchronization methods—synchronous dual‑write, asynchronous dual‑write via MQ, timer‑based SQL extraction, and real‑time Binlog replication—evaluates their pros and cons, and compares popular migration tools such as Canal, Alibaba DTS, Databus and others.
This article compares four MySQL‑to‑Elasticsearch synchronization methods—synchronous dual‑write, asynchronous dual‑write, SQL extraction, and Binlog‑based real‑time sync—evaluates their pros and cons, and reviews popular migration tools such as Canal, Alibaba DTS, Databus, Flink, CloudCanal, Maxwell, and DRDS.
This article presents UCloud's USDP‑based streaming data lake warehouse solution that leverages Flink for real‑time processing and Paimon for lake storage, detailing its architecture, advantages, practical scenarios, and providing complete SQL and Flink CDC code snippets for end‑to‑end implementation.
This guide walks through setting up Flink CDC with MySQL on SpringBoot 2.7, covering binlog configuration, Maven dependencies, Java implementation for real‑time change capture, startup options, a custom Redis sink, and a web UI for monitoring the streaming pipeline.
This article shares Kangaroo Cloud's exploration and practice of a real-time data lake, covering background, data lake concepts, challenges, solution architecture using the Shuzhan platform with Iceberg/Hudi, CDC ingestion, small file handling, cross-cluster ingestion, materialized view acceleration, and future development plans.
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.
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 article explains the limitations of using JDBC for true streaming reads in Spark and Flink, demonstrates failed attempts with MySQL, shows workarounds that revert to batch processing, and recommends Flink CDC as the practical solution for incremental MySQL ingestion.
This article explains how Kafka serves as a high‑throughput, durable messaging system, a reliable storage layer, a log‑aggregation hub, a stream‑processing engine, and a core component for CDC, system migration, monitoring, and event‑sourcing architectures.
This tutorial walks you through the fundamentals of MySQL binlog replication, installing and configuring Canal, setting up Elasticsearch, Kibana, and the IK analyzer, and then demonstrates both full and incremental data synchronization from MySQL to Elasticsearch.
This article demonstrates, with step‑by‑step examples, how to capture MySQL changes via Flink CDC and stream them in real time into Apache Doris using the Doris Flink Connector, covering CDC concepts, connector features, environment setup, SQL client usage, and data verification.
This article explains how Apache Paimon simplifies CDC data lake ingestion with one‑click, low‑cost, low‑latency pipelines, details its architecture and tag‑based Hive compatibility, provides best‑practice configurations, and presents benchmark results showing Paimon outperforming Hive and Hudi in both write and query performance.
This article explains how Alibaba Cloud's Serverless Function Compute combined with Database Change Data Capture (CDC) creates a complete, real‑time ETL pipeline, detailing the ETL model, DTS integration, architecture components, event‑driven processing, and practical use cases such as OLTP‑to‑OLAP data flow.
This article explains how to use Debezium's change data capture (CDC) capabilities to monitor MySQL binlog events, compares Canal and Debezium, outlines typical CDC use cases, and provides a complete Spring Boot integration guide with configuration, code examples, and testing procedures.
Learn how to capture MySQL data changes using Debezium's CDC capabilities within a Spring Boot application, avoiding heavyweight message brokers by leveraging binlog monitoring, configuring connectors, handling snapshots, and processing change events for use cases like cache invalidation, data integration, and simplifying monolithic architectures.
Learn step‑by‑step how to configure Alibaba’s open‑source Canal to capture MySQL binlog changes, route them through RocketMQ, and index the data into Elasticsearch, covering cluster mode, MySQL and Elasticsearch setup, Canal properties, and consumer implementation details.
This guide reviews the most popular ETL and data integration tools—including Kettle, DataX, DataPipeline, Talend, DataStage, Sqoop, FineDataLink, and Canal—detailing their core features, architectures, and typical use cases to help you choose the right solution for data migration and synchronization.
This article explains how small web projects can avoid heavyweight message middleware by using CDC technology, specifically Debezium, to monitor MySQL binlog changes, outlines why Debezium outperforms alternatives like Canal, and provides step‑by‑step Spring Boot integration with configuration, code samples, and practical use‑case scenarios.
This article explains how to use CDC technology, particularly Debezium, to capture MySQL binlog changes and process them in a Spring Boot application without adding heavyweight middleware, providing code examples, configuration details, and typical use cases.
This article presents a step‑by‑step guide on how the logistics provider Haicheng Bangda implemented a streaming data warehouse using Paimon, Flink CDC, and Kubernetes, covering business background, architecture choices, environment setup, SQL examples, troubleshooting tips, and future roadmap for their digital transformation.
This article introduces Apache Hudi as a next‑generation streaming data‑lake platform, explains its core concepts, architecture, and table types, and showcases real‑world use cases at Tencent such as CDC ingestion, minute‑level real‑time warehousing, streaming analytics, multi‑stream joins, ad attribution, and stream‑to‑batch processing, while also outlining future directions.
This guide demonstrates how to decouple data change logging from business logic in a SpringBoot application by leveraging MySQL binlog monitoring with Canal, forwarding change events through RabbitMQ, and persisting both new and old record states using Docker‑compose, configuration files, and Java client code.
This article details the background of a Flink 1.10 cluster on Huawei Cloud, the technical challenges that prompted an upgrade, a step‑by‑step migration plan to Flink 1.14.6, troubleshooting of frequent errors, precautionary measures, and the performance and operational benefits achieved after the upgrade.
This article explains the concept of Change Data Capture (CDC), outlines its common use cases, compares the main technical approaches—including timestamps, data diff, triggers, and log‑based methods—and reviews popular open‑source CDC solutions and their database‑specific configuration requirements.
This article provides a detailed tutorial on implementing various dual‑stream join techniques—including processing‑time, event‑time, and interval joins—using Flink CDC 2.2 and Flink 1.14 with the Java DataStream API, complete with code examples, SQL setup, and execution results.
This guide explains how to decouple business logic in a SpringBoot application by using Canal to listen to MySQL binlog changes, forwarding those events through RabbitMQ, and processing them with a Java client to record both new and old data for insert, update, and delete operations.
This article reviews lake‑format concepts, Apache Hudi architecture, CDC fundamentals, design considerations for CDC on lake formats, implementation details of Hudi CDC, and streaming optimizations including automated lake‑table management and a simplified StreamingSQL for Spark.
This article provides a comprehensive, hands‑on tutorial for configuring Alibaba Canal and its client‑adapter to capture MySQL binlog changes and synchronize them into Elasticsearch, covering environment setup, Docker commands, YAML configuration files, index mapping, adapter startup, and common troubleshooting scenarios.
This article explains the concept of retract streams in Apache Flink, detailing how non‑retract Kafka sources and Group‑By aggregations generate delete/insert messages, provides code examples for aggregation and sink operators, and compares retract mechanisms across aggregation and CDC sink scenarios.
This article shares practical experience comparing ClickHouse and StarRocks as real‑time data warehouses, outlines the project requirements, evaluates each system's suitability for log‑type and business‑type data, and describes CDC‑based synchronization methods from MySQL to both platforms.
Flink 1.16 introduces adaptive batch scheduling, speculative execution, hybrid shuffle, dynamic partition pruning, improved Hive SQL compatibility, advanced checkpoint mechanisms including changelog backend, and integrates CDC with Kafka and Table Store, offering faster, more stable, and easier-to-use stream‑batch processing capabilities.
Learn how to design and implement a real‑time data warehouse for an app’s AB‑test monitoring using EasyData, covering data flow layers, CDC task creation, stream table registration, Flink SQL processing, and BI reporting, with detailed steps, code snippets, and practical tips.
BitSail, ByteDance’s open‑source data integration engine, unifies batch, streaming, and incremental data synchronization across heterogeneous sources, detailing its evolution from early Flink‑based prototypes to a mature, plugin‑driven architecture with multi‑engine support, low‑cost co‑development, and robust CDC lakehouse capabilities.
BitSail, an open‑source data integration engine from ByteDance, provides a unified solution for batch, streaming, full‑load, and incremental data synchronization across heterogeneous sources, detailing its background, technical evolution, architecture, low‑cost co‑building features, compatibility strategies, and future roadmap.
This article introduces Debezium, an open‑source low‑latency change data capture platform that streams database row changes via Kafka, explains its architecture and common scenarios such as cache invalidation and CQRS, and provides step‑by‑step Docker commands to install ZooKeeper, Kafka, MySQL and the Debezium connector.
This article details Xiaohongshu's data platform architecture and three real‑time lake initiatives—log ingestion, CDC ingestion, and lake analysis—showcasing how Apache Iceberg, Flink, and custom shuffling algorithms solve small‑file and cross‑cloud challenges while enabling schema evolution and future multi‑cloud optimizations.
This article provides a comprehensive guide on using Flink CDC with Debezium to capture MySQL changes, covering serialization, adding dependencies, configuring SQL client and Java/Scala APIs, creating source and sink tables, enabling checkpoints, and streaming data into a Hudi data lake.
This article explains the challenges of point‑to‑point data sync, introduces RocketMQ Connect as a cloud‑native solution that decouples upstream and downstream, details its architecture, connectors, REST API, metrics, deployment modes, and provides a step‑by‑step guide to building custom connectors for use cases such as CDC, data lakes, and system migration.
This article details Xiaohongshu's data platform engineering, describing how Apache Iceberg is leveraged for real‑time data lake ingestion, CDC pipelines, multi‑cloud storage, small‑file mitigation, schema evolution, and future plans across storage, compute, and management within a big‑data ecosystem.
This article introduces the practice of Change Data Capture (CDC), explaining how capturing only data changes can feed downstream systems and data warehouses in near real‑time, reducing load on the source database, improving reporting latency, and supporting scalable, reliable analytics pipelines.
This article explains Change Data Capture (CDC) concepts, compares open‑source CDC tools, and shows how to leverage Alibaba Cloud EventBridge and DTS to build real‑time CDC pipelines, covering setup steps, event‑bus vs event‑stream choices, best‑practice scenarios such as CQRS, microservice decoupling, database backup, and SQL auditing.
DataBus is a real‑time data synchronization framework designed to support Oracle de‑commissioning, micro‑service migration, and heterogeneous storage engines by providing high‑availability CDC, flexible data pipelines, and seamless full‑to‑incremental migration across multiple source and target databases.
This article introduces Delta Lake as an open‑source lakehouse storage framework, explains its core features, file and metadata structures, details Alibaba Cloud EMR's enhancements and deep integration with DLF, and presents G‑SCD and CDC solutions for real‑time incremental data warehousing.
The article describes how combining Apache Flink with Hudi enables an incremental data lake that delivers near‑real‑time analytics by switching to merge‑on‑read, fixing log handling bugs, improving compaction planning, and refactoring table‑service scheduling, while showcasing use cases such as CDC ingestion, data quality control, and real‑time materialized views, and outlines future enhancements like optimistic concurrency and unified schema evolution.
This article introduces the concept and practice of Change Data Capture (CDC), explaining how it captures database changes to provide real‑time incremental data for analytics and reporting without impacting source performance, and outlines modern CDC methods, challenges, and production‑ready system requirements.
This article introduces the concept of Change Data Capture (CDC), explains why traditional batch reporting strains resources, describes how CDC captures only data changes to keep source databases performant, and outlines modern CDC architectures, production‑ready considerations, and best‑practice guidelines for building reliable data pipelines.
This article explains how Apache Flink integrates with Apache Hudi to enable real‑time data lake ingestion, covering the evolution from traditional data warehouses to data lakes, Hudi’s core concepts such as timeline and file grouping, copy‑on‑write vs merge‑on‑read modes, and Flink’s CDC‑based ETL pipeline.
This article introduces Delta Lake as an open‑source storage layer for lake‑house architectures, explains its key features, file and metadata structures, and details how Alibaba Cloud EMR and Data Lake Formation integrate and extend Delta Lake with advanced capabilities such as G‑SCD, CDC, performance optimizations, and future roadmap.
Delta Lake, an open‑source storage layer from Databricks, provides ACID transactions, data versioning, schema evolution, and unified batch‑stream processing, with a detailed file structure and metadata mechanism, while Alibaba Cloud EMR enhances it with advanced DML, performance optimizations, deep DLF integration, and solutions for G‑SCD and CDC.
The article explains the fundamentals of Change Data Capture (CDC), describing how capturing DML changes from relational databases like MySQL or PostgreSQL can provide incremental, near‑real‑time data for analytics and reporting while preserving source performance, and outlines modern CDC architectures, transaction‑log based extraction, and production‑ready design considerations.
This article introduces the concept and practice of Change Data Capture (CDC), explaining why CDC is needed for real‑time analytics, how it works by capturing DML changes, modern approaches using transaction logs, and key considerations for building a production‑ready CDC system.
This article details a comprehensive TiDB cluster splitting project, covering background, challenges, backup and restore tools, multi‑stage migration steps, binlog incremental synchronization, CDC integration, and practical tips to ensure data consistency and minimal service impact.
Shopee’s Real‑time Checking System provides configurable, non‑intrusive data consistency verification for micro‑services by capturing change events via CDC, streaming them through Kafka, applying flexible rules and expressions, and instantly alerting mismatches, delivering second‑level detection while scaling to tens of thousands of checks per second.
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.
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 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 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.
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 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 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 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 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.
This article provides a detailed comparison of three popular open‑source change data capture tools—Debezium, Flink CDC, and Canal—covering their underlying principles, architecture, deployment options, performance characteristics, and suitability for real‑time data synchronization in big‑data environments.
This article introduces Flink CDC 2.0, explains its core design—including chunk splitting for full‑load and incremental reads—provides a Flink SQL example, and walks through the MySQL CDC source implementation with detailed code snippets and processing logic.
This article explains CDC fundamentals, compares query‑based and log‑based capture, describes typical CDC‑to‑lake architectures using Pulsar and Hudi, dives into Hudi's core design, optimization techniques, and future roadmap, and provides practical insights for building scalable data lakes.
This article traces two decades of application data architecture evolution, comparing traditional single‑system LAMP designs with modern multi‑component cloud‑native stacks, and offers practical guidance on scaling, component selection, CDC‑based data derivation, and cloud‑native implementations such as Tablestore.
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.
This article summarizes frequent problems encountered when using Flink CDC with MySQL—including Kafka version conflicts, checkpoint timeouts, permission errors, global lock issues, and DDL parsing failures—and provides practical configuration tweaks and code examples to resolve them.
This article explains how to construct a lake‑house architecture using Apache Iceberg, detailing the migration from Hive, Flink‑SQL integration, proxy user support, CDC handling, copy‑on‑write sinks, and the resulting benefits for near‑real‑time data visibility and unified batch‑stream processing.
This guide walks through preparing the environment, creating a MySQL source table, configuring Flink CDC to ingest data into an Apache Hudi table, and then querying the Hudi data using both Hive and Spark‑SQL, including handling of partitions, realtime input formats, and required configuration settings.
This article outlines a comprehensive, step‑by‑step approach for migrating massive tables—up to billions of rows—through sharding, dual‑write, data synchronization, validation, testing, and controlled switch‑over to ensure minimal impact on production services.
Learn how to capture and stream real‑time database changes using Debezium’s distributed CDC framework, configure MySQL binlog, integrate the embedded engine with Spring Boot, and process change events with sample code and Docker setup for robust data pipelines.
This article explores the role of message queues in microservice architectures, discussing their advantages such as decoupling, asynchronous processing, and load shedding, while also addressing design challenges like concurrency, ordering, duplicate handling, and transactional messaging with solutions including Kafka partitions, outbox patterns, CDC, and RocketMQ.
This article introduces four change‑data‑capture solutions—Canal, Maxwell, Databus, and Alibaba Data Transmission Service (DTS)—explaining their principles, processing steps, features, and practical advantages for real‑time data synchronization and migration in big‑data environments.
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 comprehensive guide explains how to migrate Oracle databases to MySQL by detailing type differences, migration steps, performance tuning, and validation techniques, while highlighting common pitfalls such as data‑type mismatches, character‑set issues, LOB handling, and transaction isolation nuances.
The article examines the key challenges of real‑time data aggregation—data freshness, timely processing, and result visibility—and surveys common solutions such as timestamp‑based sync, CDC, full and incremental computation, storage formats, and trigger mechanisms.
The article reviews common cache‑side data‑sync patterns, highlights their inconsistency and data‑loss risks under high load, and presents JD's solution that combines Cache‑Aside, Change Data Capture, message queues, delayed consumption, versioning, and persistence to ensure eventual consistency between cache and relational databases.
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.
