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.
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.
The article compares three approaches—synchronous dual write, asynchronous dual write with a message queue, and CDC via Canal—to keep Elasticsearch and a relational database consistent for cross‑database aggregated full‑text search, outlining their steps, advantages, and drawbacks.
This article describes how FluentSpeak replaced a DataX master‑slave pipeline with a CDC‑plus‑Delta Lake solution on Alibaba Cloud EMR, detailing architecture choices, streaming SQL merge logic, monitoring, challenges, and the resulting cost and latency improvements.
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 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.
This article explains how to replace daily offline MySQL‑to‑Hive sync with a CDC pipeline using Alibaba’s Canal to capture binlog events, Kafka for transport, and LinkedIn’s Camus (via a custom writer) to load data into Hive, detailing configuration and deployment steps.
Debezium is an open‑source distributed platform built on Apache Kafka that captures row‑level changes from databases via change data capture, providing source connectors, an optional embedded engine, and features like low‑latency streaming, snapshots, filtering, masking, and integration with various sink systems.
This article introduces four change‑data‑capture solutions—Canal, Maxwell, Databus, and Alibaba Cloud DTS—explaining their principles, processing steps, key features, and advantages for real‑time data replication and synchronization across diverse database systems in modern data engineering environments.
The article explains why achieving data consistency across multiple microservices is challenging, reviews the limitations of two‑phase commit, and presents practical techniques such as the Saga pattern, reconciliation, event logs, orchestration vs. choreography, and change‑data‑capture to keep distributed systems eventually consistent.
Facing MySQL performance bottlenecks at massive scale, Xiaomi built the LCSBinlog service that captures MySQL binlog events, streams them through the Talos platform, and writes to Kudu for real‑time BI, detailing architecture, job scheduling, consistency guarantees, use cases, and troubleshooting lessons.
Debezium is an open‑source distributed platform built on Apache Kafka that turns existing databases into real‑time event streams by capturing row‑level changes via change data capture, offering source and embedded connectors, flexible topic routing, and features such as snapshots, filtering, masking, and monitoring.
This article explains how CB‑SQL implements follower reads by using safe (closed) timestamps, describes the CT update mechanism with a Minimum Proposal Tracker, and discusses routing, replica read validation, timestamp forwarding, range split/merge handling, and recovery strategies for consistent distributed reads.
This article explores the core components of data systems, the evolution toward lightweight, intelligent big data architectures, the distinction between primary and secondary storage, challenges of data replication, and how Alibaba Cloud's Tablestore implements advanced features such as storage‑compute separation, CDC, and multi‑model indexing for scalable, cost‑effective structured big data storage.
The article explains CB‑SQL’s distributed changefeed architecture, its CDC implementation, the challenges of horizontal scalability and transactional ordering, and the innovative RangeFeed mechanism that enables ordered row‑level streams, resolved timestamps, and seamless integration with external systems like Kafka.
The article introduces CB‑SQL, JD Retail’s next‑generation elastic database based on CockroachDB, and comprehensively explains its overview, layered architecture, storage replication, cluster management, MVCC, hybrid logical clocks, lock‑free distributed transactions, CDC changefeeds, and SQL interface support.
The article presents DTLE, an open‑source MySQL data‑transfer middleware that extends replication capabilities with high‑performance CDC, multi‑topology support, cloud‑to‑cloud synchronization, and robust cluster management, while comparing it with other open‑source solutions and showcasing real‑world demos.
The article presents a CDC solution that combines Alibaba’s Canal for real‑time MySQL binlog capture into Kafka with LinkedIn’s Camus for hourly Kafka‑to‑Hive loading, then merges snapshots and incremental binlog data to accurately and efficiently rebuild ODS tables, supporting sharding and delete events.
This article details the challenges of moving from SQL Server to MySQL, compares offline and online migration approaches, presents a complete toolbox—including ETL utilities, consistency checkers, CDC configuration, and rollback mechanisms—and shares practical lessons from a large‑scale production migration.
This article explains how to achieve near‑zero‑downtime online migration from SQL Server to MySQL by leveraging Change Data Capture (CDC) and the Alibaba Yugong tool, covering CDC principles, configuration, performance tuning, and practical migration steps.
This article details how Vimur, a Kafka‑based real‑time data pipeline, addresses the challenges of service splitting, database sharding, data migration, and synchronization by using CDC, a unified Avro format, and a change distribution platform to support search indexing, cache refresh, and reactive architectures.
This article explains the design and implementation of the DWS platform, detailing its log‑driven architecture with Dbus, Wormhole, and Swifts, the technical choices behind real‑time data extraction, transformation, and delivery, and real‑world use cases in finance.
This article explains why DB2 customers must upgrade to avoid EOS risks, compares replication options, and provides a detailed CDC‑based step‑by‑step process to achieve minute‑level downtime, zero performance impact, and fast rollback for large‑scale DB2 version migrations.
