This case study details how a high‑traffic Kafka logging cluster was optimized by analyzing low compression ratios, tuning Filebeat parameters, adjusting memory queues and round‑robin settings, and validating the changes through gray‑scale tests, resulting in up to 35% higher throughput and significant resource savings.
This article provides a concise overview of Kafka cluster fundamentals, describing how multiple brokers form a distributed system, the role of topics and partitions for scalability, and illustrating data distribution and replication with a three‑broker example.
Learn how to transform PHP logs into real‑time, structured events by implementing a log event pipeline that includes JSON logging, lightweight collectors like Filebeat, streaming platforms such as Kafka or Flink, enrichment, and visualization with Grafana, enabling instant monitoring, alerting, and data‑driven decisions.
This article explains the essential components of a Kafka cluster—including producers, consumers, brokers, Zookeeper, and the KRaft controller—detailing how topics, partitions, and replication enable high‑throughput, scalable, and fault‑tolerant distributed messaging.
This article explains Kafka’s core architecture—producer, broker storage, and consumer groups—detailing how messages are partitioned, buffered, replicated, and consumed to achieve high throughput, low latency, and scalable real‑time data processing.
This article explains how to design and implement a generic asynchronous processing SDK for Java backend services, covering its purpose, advantages, underlying principles, component choices, database schema, configuration, usage patterns, and best‑practice notes, with code examples and diagrams.
This case study details how Hypergryph leveraged Alibaba Cloud EMR Serverless StarRocks, Flink, and Kafka to replace a ClickHouse data warehouse with a high‑performance, elastic, and easy‑to‑operate real‑time analytics platform that dramatically improved query speed, stability, operational efficiency, and cost for their gaming business.
This article explains Kafka's storage architecture, including its partition‑based log files, sequential append writes, log segment management, index files, and configurable time‑ and size‑based retention policies that together enable ultra‑high write throughput while controlling disk usage.
This article provides a comprehensive guide to building enterprise‑grade, highly available Kafka clusters, covering architecture design, hardware planning, production‑level broker configurations, ISR management, monitoring, fault‑tolerance procedures, rolling upgrades, capacity planning, and automation scripts for seamless operations.
Facing daily data growth from millions to 150 billion records, Joydata‑U transformed its analytics platform through three architectural stages—Hadoop, Hadoop + Trino, and finally StarRocks—introducing resource isolation, Flat JSON acceleration, and Bitmap indexing to cut query latency by up to seven times and achieve sub‑2‑minute data freshness across BI, ad‑tech, game analytics, and CRM workloads.
This article shares 360 Group's systematic Kafka capacity‑planning methodology, covering hardware performance analysis, disk I/O benchmarking, cluster configuration, load‑testing procedures, observed write‑read dynamics, and practical recommendations for reliable Kafka deployments.
This guide walks you through the core Kafka commands for starting and stopping the service, creating, listing, describing, and deleting topics, as well as producing and consuming messages, while explaining key parameters such as Zookeeper, partitions, and replication factors.
When Kafka backs up and Logstash fails to write to Elasticsearch with a 403 cluster_block_exception, the index is locked by a write block, which can be resolved by clearing the index.blocks.write setting via the Elasticsearch API or Kibana dev tools.
This guide explains how to efficiently produce messages with TDMQ CKafka, covering topic creation, partition sizing, retry policies, asynchronous sending, key/value handling, batch tuning, sticky partitioning, idempotence, ACK settings, code examples, and strategies to avoid data skew and ensure high‑throughput, low‑latency messaging.
This guide explains why Kafka consumer throughput matters, then details practical techniques—including multi‑threaded consumption, scaling with consumer groups, client parameter tuning, and batch processing—to dramatically increase throughput while maintaining reliability in high‑concurrency, large‑scale data pipelines.
This article explains how Apache Kafka attains million‑level write throughput by sequentially appending messages to log files, leveraging operating‑system page cache and asynchronous disk flushing, and details the step‑by‑step data flow from producer to broker to storage.
This article recounts a series of Kafka message backlog incidents in a restaurant ordering system and explains how targeted optimizations—batch database queries, index tuning, data archiving, and thread‑pool scaling—eliminated the delays and restored reliable kitchen display performance.
This article explains how to use Spring's TransactionSynchronizationManager to detect active transactions and reliably send Kafka messages either immediately or after transaction commit, illustrated with a payment‑system use case and complete code examples.
This article explains how Kafka achieves extremely high throughput by using a Reactor‑based non‑blocking I/O model, zero‑copy data transfer, sequential disk writes, memory‑mapped files, sparse indexing, partition load‑balancing, compression, batch processing, and a lock‑free offset design.
Kafka ensures that a producer’s repeated message sends, caused by network glitches or broker failures, result in only one persisted record per partition by using a unique Producer ID, monotonically increasing sequence numbers, and broker-side tracking of the latest committed sequence for each PID‑partition pair.
This article explains why Kafka’s at‑least‑once delivery can cause duplicate message processing, outlines the business risks of such duplicates, and presents four practical solutions—including idempotent design, manual offset commits, exactly‑once semantics, and dead‑letter queues—to ensure reliable consumption.
This article explains how Kafka sustains massive traffic by writing logs sequentially to disk, leveraging the operating system’s page cache for fast in‑memory writes, employing zero‑copy techniques like sendfile to avoid user‑space copying, and batching messages to reduce network overhead, thereby delivering high‑throughput, low‑latency streaming.
This article explains Kafka’s high‑performance distributed streaming architecture, key components such as topics, partitions, producers, consumers, brokers, offsets, and ZooKeeper, and provides step‑by‑step workflows for producers and consumers along with performance‑testing tips and Maven setup.
This guide walks you through installing Logstash and the logstash-output-oss plugin, then shows a complete logstash.conf that reads from Kafka, filters and enriches events, and outputs them to Alibaba Cloud OSS with compression, rotation, and recovery settings.
This article explains how to improve front‑end data transfer using the browser's navigator.sendBeacon API and how to streamline back‑end processing with Go, Kafka, and ClickHouse to achieve higher performance and stability in feature‑heavy web applications.
This guide presents a step‑by‑step Kafka learning roadmap covering core concepts, architecture, configuration, monitoring tools, practical project ideas, advanced components like Streams and KSQL, plus code samples and resource recommendations to help beginners become proficient in real‑time data streaming.
This article introduces a non‑intrusive asynchronous processing SDK that leverages Spring transaction events, Kafka queues, and MySQL tables to ensure reliable execution, eventual consistency, and automatic retry without impacting the main business flow.
This article provides a comprehensive overview of Kafka, covering its high‑throughput distributed messaging model, key components such as producers, consumers, brokers, topics, partitions, and metadata management, as well as the end‑to‑end workflow from production to consumption.
When Kafka consumers can't keep up with producers, messages pile up, storage pressure rises, latency spikes, and downstream services suffer, but increasing consumer threads, batching, and asynchronous processing can alleviate these issues and restore system stability.
Facing petabyte‑scale log traffic, the Qunar team identified low compression rates in their Kafka‑Filebeat pipeline as the main bottleneck and, through systematic tuning of batch size, memory queues, and round‑robin settings, achieved a 35% reduction in traffic and a 30‑42% drop in request volume while raising per‑minute throughput by 35%.
This article introduces a generic asynchronous processing SDK for Java, explaining its purpose, advantages, underlying principles, component choices such as Kafka, XXL‑Job, MySQL, and Spring AOP, and provides detailed configuration, database schema, usage examples, and best‑practice guidelines to achieve reliable, non‑blocking execution and eventual consistency in backend systems.
This article walks through the common frustrations of IDE and environment setup for Spring Boot projects, explains version compatibility between Spring Cloud, Spring Boot, and Kafka, demonstrates how Maven and auto‑configuration simplify dependencies, and provides ready‑to‑use code snippets and useful tooling recommendations for backend developers.
This article examines the challenges of high‑concurrency write scenarios in ad‑billing systems, explains why row‑level locking becomes a bottleneck, and presents a step‑by‑step solution using Kafka for asynchronous peak shaving, parallel consumer processing, database sharding, hotspot dispersion, and batch deduction to achieve reliable real‑time balance updates.
This article explains the three message delivery semantics, the common causes of duplicate messages in queue systems, and presents concrete producer‑side, broker‑side, and consumer‑side deduplication techniques for Kafka and Pulsar, including code samples and best‑practice recommendations.
This article shares practical Kafka production insights, covering architecture overview, producer throughput tuning, message loss prevention, broker and consumer configurations, duplicate consumption avoidance, backlog mitigation, ordering guarantees, and the mechanics of consumer group rebalancing, helping engineers build stable, high‑performance streaming pipelines.
This article explains how Kafka achieves high concurrency through its distributed broker cluster, partitioned topics, sequential log writes, message compression, asynchronous producer mechanisms, and OS page‑cache techniques, illustrating the combined architectural and performance optimizations that enable massive throughput.
This article explains Kafka's event streaming concept, detailing events and streams, core components such as producers, topics, partitions, consumers, persistence, and typical real‑time data pipeline, event‑driven architecture, stream processing, and log aggregation use cases, highlighting its role as a foundational big‑data infrastructure.
This tutorial walks through downloading, extracting, configuring, starting, creating topics, producing and consuming messages, and finally stopping Apache Kafka 3.8.1 on a Linux system, including all necessary command‑line instructions.
This article examines the typical reasons Kafka messages are lost across producers, brokers, and consumers, and provides detailed configuration recommendations and best‑practice solutions to significantly reduce the risk of data loss in distributed streaming systems.
This article explains the fundamentals, architectures, key concepts, advantages, and disadvantages of Kafka, RabbitMQ, RocketMQ, and ActiveMQ, and provides practical guidance on selecting the most suitable message queue for various application scenarios such as high‑throughput logging, financial transactions, or small‑scale services.
This article details practical techniques for boosting Kafka producer throughput, covering batch sending optimization, linger.ms tuning, asynchronous send handling, compression options, and concurrent sending strategies, while providing code examples and performance considerations for high‑throughput streaming applications.
This article explains why Kafka is widely adopted by top companies, outlines its high throughput, scalability, and durability, and describes key real‑time data pipeline, stream processing, and big‑data integration scenarios, concluding that mastering Kafka is essential for modern backend and data engineering roles.
This article explains the CQRS architectural pattern, its benefits and pitfalls, and provides a step‑by‑step guide to implementing CQRS with Spring Boot, Axon, and Kafka, including command and query handling, event sourcing, and practical considerations for microservice architectures.
This article explains Kafka's core concepts—including topics, partitions, log segmentation, offset management, and acknowledgment levels—while also providing a step‑by‑step guide to deploying Zookeeper, Kafka, Filebeat, and ELK, troubleshooting common issues, and visualizing logs in Kibana.
This article reviews six mainstream solutions for keeping MySQL and Elasticsearch in sync—including synchronous double‑write, asynchronous MQ‑based double‑write, Logstash polling, Canal binlog listening, DataX batch migration, and Flink stream processing—detailing their scenarios, advantages, drawbacks, and practical code examples to guide optimal technical selection.
Airbnb's media team engineered a scalable postcard generation pipeline that matches destinations, handles localized text layout, and delivers high‑performance images across iOS, Android, Web, and external messaging platforms using flexible templates, async processing, and pre‑generation strategies.
Kafka 4.0 replaces ZooKeeper with the native KRaft (Kafka Raft) consensus layer, simplifying deployment, boosting scalability, cutting latency, and introducing a new incremental consumer rebalance protocol and share‑group model that together reshape how distributed streaming applications are built and operated.
This technical case study details how a high‑traffic Kafka logging cluster was optimized by adjusting Filebeat and Kafka parameters, increasing compression batch size, and tuning Kubernetes settings, resulting in significant reductions in request volume, network traffic, CPU usage, and overall resource consumption.
This article explains how to import 1 billion 1 KB log records stored in HDFS or S3 into MySQL by analyzing single‑table limits, using batch inserts, choosing storage engines, sharding, optimizing file‑reading methods, and coordinating distributed tasks with Redis, Redisson, and Zookeeper to ensure ordered, reliable, and high‑throughput data loading.
Grab replaced its fragmented Grafana‑Superset stack with a StarRocks‑backed Iris platform, achieving over ten‑fold query speedups, 40% lower resource usage, and a unified real‑time and historical data store for Spark observability across its Southeast Asian super‑app ecosystem.
Kafka’s internal mechanisms for leader election, partition assignment, and file storage are explained, covering how the Controller uses ZooKeeper, the ISR-based leader selection process, partition distribution strategies, segment file structures, and the evolution of offset management from Zookeeper to the __consumer_offsets topic.
This article compares six mainstream MySQL‑to‑Elasticsearch synchronization methods—synchronous double‑write, asynchronous MQ, Logstash polling, Canal binlog listening, DataX batch sync, and Flink streaming—detailing scenarios, code samples, advantages, drawbacks, and practical selection guidance for developers.
This guide reviews common backend interview topics, explaining TCP TIME_WAIT behavior, multi‑port listening, full page load steps, Redis data types, Kafka consumer sizing and at‑most‑once semantics, the CAP theorem, Singleton usage, C++ std::map complexity, and an O(n) doubly‑linked list reversal algorithm.
Kafka attains million‑level message throughput by writing logs sequentially to disk, leveraging OS page cache for batch flushing, sending messages in large batches, and employing zero‑copy techniques that move data directly from disk to network sockets, dramatically reducing I/O overhead and CPU usage.
This article explains how Ctrip’s hybrid‑cloud FinOps billing system uses a custom Kafka Gatekeeper to detect, locate, and automatically remediate data‑quality issues across dozens of self‑built PaaS services, improving coverage, timeliness, and responsibility attribution while supporting high‑availability deployments.
This guide walks through initializing a Spring Boot project, handling IDE setup challenges, managing version compatibility between Spring Cloud, Spring Boot, and Kafka, and provides practical code examples for global exception handling, logging, CORS configuration, Swagger integration, and useful development tools.
This article visually breaks down Kafka’s fundamental concepts—including producers, consumers, topics, consumer groups, partitions, and cluster architecture—explaining how messages flow, are stored, and achieve load balancing and ordering within a distributed streaming system.
This article explains common causes of duplicate message consumption in high‑traffic systems and presents a three‑layer defense—producer idempotence, broker de‑duplication, and consumer idempotent design—plus monitoring and reconciliation strategies to achieve reliable exactly‑once processing.
This article explains how RocketMQ, a Chinese‑origin message queue, simplifies Kafka’s architecture while adding powerful features such as tag‑based filtering, transactional messaging, delayed and dead‑letter queues, and a unified commit‑log storage model, making delayed processing and high‑throughput scenarios easier to implement.
This article introduces a reusable asynchronous processing SDK built on Spring, Kafka, and MySQL that leverages @AsyncExec annotations, transactional event listeners, and configurable thread pools to ensure reliable, non‑blocking execution, data consistency, and fault‑tolerant handling of business logic in backend applications.
Kafka 4.0 introduces KRaft, a ZooKeeper‑free metadata layer built on the Raft consensus algorithm, detailing role transitions, leader election, log replication, controller and broker responsibilities, and fault‑tolerance mechanisms, enabling a more scalable and self‑managed architecture for large‑scale distributed streaming.
This article compares Kafka, RocketMQ, and RabbitMQ, detailing their architectures, performance characteristics, strengths, and ideal use‑cases to help engineers select the most suitable message‑queue solution for high‑throughput, fault‑tolerant, and real‑time processing scenarios.
This article compiles a comprehensive collection of over two hundred interview questions covering Linux system administration, Nginx web server, MySQL databases, Redis caching, Kafka messaging, and core network security topics, providing a one‑stop resource for candidates preparing for major tech company interviews.
Didi replaced its legacy agent with Alibaba’s open‑source iLogTail, re‑architected it to use a shared thread‑pool and SIMD‑accelerated parsing, rewrote critical plugins in C++ and added robust Kafka retry logic, achieving over twice the throughput while cutting CPU usage by more than half and maintaining near‑zero latency at massive scale.
This guide walks you through deploying the Kubernetes Event Exporter, packaging its Helm chart and Docker image, configuring Kafka TLS secrets, setting up Logstash to ingest events and forward them to Elasticsearch, creating an index template, and verifying the end‑to‑end pipeline.
This article compares Kafka and RabbitMQ across design philosophy, performance, data model, delivery semantics, ecosystem, and typical use cases, helping readers choose the right messaging middleware for high‑throughput or reliable delivery scenarios.
iQIYI transformed its real‑time data warehouse by replacing a costly Kafka‑based Lambda stack with a unified stream‑batch Iceberg lake, cutting storage expenses by 90%, halving compute costs, extending data retention, and delivering minute‑level freshness for 90% of use cases while preserving second‑level processing where needed.
Apache Kafka 4.0 eliminates Zookeeper with the KRaft consensus protocol, upgrades Java requirements to 17, introduces a next‑generation consumer rebalance protocol, adds Share Group queue semantics, and bundles numerous performance, security, and API improvements for modern streaming workloads.
This article explores why message queues can lose consistency, presents concrete solutions such as transactional two‑phase commits, persistence settings, replica configurations, unique IDs, idempotent designs, and dead‑letter queues, and shares ten practical lessons drawn from real‑world incidents.
Kafka 4.0 marks a milestone release that replaces ZooKeeper with the KRaft consensus engine, improves scalability and performance, introduces a server‑side consumer‑group protocol, adds shared‑group queue capabilities, and updates Java requirements and documentation, delivering a more robust and flexible streaming platform.
Apache Kafka 4.0 introduces a groundbreaking KRaft architecture that removes ZooKeeper, a revamped consumer‑group protocol that dramatically speeds up rebalancing, a new queue mode for point‑to‑point messaging, upgraded Java requirements, streamlined APIs, and numerous performance and security enhancements, reshaping both development and operations for large‑scale streaming workloads.
This article provides a comprehensive overview of Apache Kafka, covering its core concepts, architecture, common use cases, installation steps, and essential command-line operations for managing topics and brokers in production environments and ensuring reliability.
This article explores eight common scenarios for using message queues in backend development, covering asynchronous processing, service decoupling, traffic shaping, delayed tasks, log aggregation, distributed transactions, remote calls, and broadcast notifications, each illustrated with Java, RocketMQ, and Kafka code examples.
Message queues can deliver duplicates under at‑least‑once semantics, so to protect idempotent business logic such as orders and payments you should combine producer‑side idempotence (e.g., Kafka’s enable.idempotence), broker‑side deduplication (e.g., Pulsar), and a consumer‑side guard using unique IDs stored in a database or Redis.
Message queues can cause duplicate messages that affect idempotent business processes, so this article explains the three delivery semantics (At Least Once, Exactly Once, At Most Once), the causes of duplication, and practical deduplication techniques for producers, brokers (Kafka, Pulsar), and consumers using code examples.
This article walks through setting up a Spring Boot project, handling version compatibility with Spring Cloud, Kafka, and Maven, demonstrates global exception handling, logging, CORS and Swagger configurations, and recommends useful backend utilities such as embedded Redis, MyBatis Plus, and Redisson.
This guide walks you through four progressive stages—basic configuration, SSL encryption, SCRAM authentication, and combined SSL+SASL—showing how to harden Kafka with certificates, keystores, and client settings to achieve financial‑level protection.
This comprehensive guide explains the role of message queues in distributed systems, compares the architectures and core concepts of Kafka, Pulsar, RocketMQ, RabbitMQ, and NSQ, and provides practical criteria for selecting, configuring, and operating a message‑queue solution in high‑performance, high‑availability environments.
The article shares DJI's impressive compensation data, details a 50‑minute Java backend interview covering concurrency tools, locks, CountDownLatch, ConcurrentHashMap, Kafka ordering, zero‑copy, Spring AOP, filters vs. interceptors, and provides code examples and explanations for each concept.
This article combines a DJI interview experience—including salary and bonus details—with an in‑depth guide to Java concurrency tools, lock implementations, SpringBoot AOP, filters vs. interceptors, Kafka ordering, zero‑copy techniques, and related backend development best practices.
This step‑by‑step guide shows how to use a Helm chart to deploy Filebeat in a Kubernetes cluster, automatically collect container logs, and forward them to a Kafka cluster for reliable, scalable observability.
This article provides a comprehensive technical walkthrough of KafkaConsumer, covering Java configuration code, delivery semantics (at‑most‑once, at‑least‑once, exactly‑once), offset commit strategies, rebalance mechanisms, partition assignment algorithms, thread‑safety concerns, and internal poll implementation, followed by unrelated promotional content.
Message queues act as asynchronous communication bridges in distributed systems, and this article examines three leading solutions—Kafka, RabbitMQ, and RocketMQ—detailing their performance, scalability, reliability, strengths, drawbacks, and suitable use‑cases to guide developers in selecting the right technology.
DeWu’s observability platform faced scalability, cost, and operational challenges from petabyte‑scale trace data, prompting a shift to a storage‑compute separated architecture that leverages AutoMQ’s Kafka‑compatible service and ClickHouse Enterprise’s SharedMergeTree engine, ultimately achieving up to 50% cost reduction and five‑fold cold‑read performance gains.
This article explains eight typical scenarios for using message queues—including asynchronous processing, service decoupling, traffic shaping, delayed tasks, log collection, distributed transactions, remote invocation, and broadcast notifications—providing clear explanations and Java code snippets for each case.
This article describes how a legacy SSM‑based backend reporting service was refactored by archiving log data, leveraging Redis for hourly counters, and scheduling synchronization tasks to dramatically reduce query time for millions of records without adding external processing frameworks.
This article describes the challenges of data quality in Ctrip’s hybrid‑cloud FinOps billing system and presents the design, implementation, and high‑availability deployment of a custom Kafka Gatekeeper proxy that performs pre‑validation, configurable rules, self‑service dashboards, and automated alerts to improve coverage, timeliness, and responsibility attribution.
This article details how a legacy reporting module suffering from slow page loads was refactored by archiving log data, caching hourly counts in Redis, and using scheduled synchronization to dramatically improve query performance without adding new middleware.
This article explores the design and implementation of time wheel algorithms in Netty and Kafka, comparing their single‑layer and multi‑layer approaches, analyzing performance trade‑offs, and detailing how these systems achieve O(1) scheduling for massive delayed tasks while avoiding empty ticks.
This comprehensive guide explains Kafka’s architecture, core APIs, topics and partitions, deployment steps, multi‑broker clustering, and practical use cases such as messaging, log aggregation, stream processing, and data import/export with Kafka Connect, providing a hands‑on tutorial for developers and engineers.
This article explains how to leverage Spring's TransactionSynchronizationManager to detect active transactions, register synchronization callbacks, and asynchronously send Kafka messages only after a transaction successfully commits, illustrated with a payment‑system example and complete Java code snippets, while also noting thread‑local considerations and promotional offers.
This article examines the causes of message queue disorder in distributed systems and presents practical solutions such as ordered messaging, pre‑consumption checks, state machines, and monitoring to improve system stability and data consistency.
Flink Forward Asia 2024 highlighted the limitations of Kafka for real‑time analytics—lack of updates, poor data exploration, costly back‑tracking, and high network overhead—while introducing Fluss, a columnar streaming storage that offers low‑latency reads, CDC, lake‑stream integration, and efficient Delta Join for scalable, fast analytics.
In this interview, Xiaohongshu data engineer Jianchen recounts his evolution from a computer‑science student discovering open‑source through MIT6.824 to contributing to SOFAJRaft and Apache RocketMQ, detailing his OSPP projects, the decision to join Xiaohongshu, and his work on a cloud‑native Kafka engine that cut storage and compute usage by half.
This article walks through initializing a Spring Boot project, handling version compatibility between Spring Cloud, Spring Boot, and Kafka, demonstrates common scaffolding code such as global exception handling, logging, CORS configuration, and recommends useful tools like Maven, embedded Redis, MyBatis‑Plus, and MapStruct for backend development.
This article provides a comprehensive overview of Kafka, covering its overall structure, key components such as brokers, producers, consumers, topics, partitions, replicas, leader‑follower mechanics, logical and physical storage models, producer and consumer workflows, configuration parameters, partition assignment strategies, rebalancing, log retention and compaction, indexing, zero‑copy transmission, and the reliability concepts that ensure data durability.
An extensible asynchronous processing SDK is presented, leveraging Spring transaction events, Kafka queues, XXL‑Job scheduling, and a non‑intrusive architecture to ensure high performance, data safety, and eventual consistency while simplifying integration across Java backend services.
This article explains how to monitor and interpret Full GC in Flink TaskManagers, detect and address data skew through proper data distribution and parallelism settings, and recommends aligning consumer parallelism with Kafka partitions, while also providing practical tips for using tools like Prometheus and Arthas.
This article explains how to leverage Spring's TransactionSynchronizationManager to detect active transactions and register synchronization callbacks so that Kafka messages are sent asynchronously only after a transaction successfully commits, ensuring data consistency without impacting the main business flow.
This article demonstrates how to use ActionOMS to capture incremental changes from OceanBase, stream them to Kafka in various formats, and employ Flink to deduplicate and aggregate transaction data into a daily summary, illustrating a complete real-time data pipeline for financial use cases.
