This article outlines Zhihu’s practical use of big‑data FinOps, describing its hybrid‑cloud architecture, the challenges of multi‑vendor cost management, and how a systematic billing system launched in 2022 drives sustainable cost reduction across the organization.
This article breaks down Kafka’s architecture, explaining how sequential I/O, zero‑copy, batching with compression, and partition‑based horizontal scaling combine to deliver ultra‑high throughput, low latency, and strong reliability for handling billions of messages.
This article details how HuoLala built a high‑performance user profiling platform on Apache Doris, redesigning data models, leveraging bitmap storage, and applying query‑level optimizations to achieve up to 30‑fold speed gains, lower memory usage, and scalable real‑time analytics.
The article outlines the problems of early data services, proposes a two‑step data warehouse solution—abstracting a data model and extracting, cleaning, and storing data from MySQL, MongoDB, and Elasticsearch—then details storage selection, dimensional modeling, incremental ETL, and Airflow workflow management.
This article explains Apache Hudi's architecture, including its timeline mechanism, file layout, indexing strategies, table types (COW and MOR), query options, storage format versioning, backward compatibility, and key configuration settings for managing data lake tables.
This article explains how to reduce intermediate tables, simplify dependencies, and improve execution efficiency in Spark SQL pipelines by using session‑level temporary views and regex‑based SQL parsing to automatically merge and rewrite DAG tasks in large‑scale data platforms.
Ctrip's User Behavior Tracking (UBT) system, handling 30 TB of daily data, moved from ClickHouse to StarRocks' compute‑storage separated architecture, cutting average query latency from 1.4 seconds to 203 ms, halving storage, reducing nodes from 50 to 40, and boosting write throughput to 3 million rows per second.
This article introduces Douyin Group’s one‑stop Data Asset Management Platform, explains why the company focuses on data assets rather than raw metadata, and details the evolution, architecture, applications, and future outlook of its comprehensive big‑data lineage system.
This article details Baidu APP's massive data‑warehouse overhaul, describing the two‑step strategy that stabilized log cleaning, modernized the ETL framework, introduced wide‑table architectures, and implemented tiered storage to dramatically improve processing speed, reliability, and cost efficiency for petabyte‑scale workloads.
This article shares a data‑governance roadmap for small and medium banks, covering industry pain points, high‑quality data sets, a three‑step governance path, data standards, metadata management, master‑data strategy, business data modeling, a hybrid Greenplum‑Hadoop platform, quality monitoring, and a maturity assessment framework.
As data volumes explode, enterprises struggle with the high hardware, performance, operational, and migration costs of traditional OLAP warehouses, but ByteHouse’s cloud‑native architecture offers a cost‑effective, high‑performance solution that dramatically reduces both explicit and hidden expenses.
Ant Group shares its practical experience in big data governance, outlining five key focus areas—architecture, security, compliance, quality, and value—through four structured sections and detailed discussions on data quality and storage governance, while also exploring future challenges and the economics of data.
This article explains how Zhihu’s big‑data platform applies FinOps principles and a mixed‑cloud strategy to overcome multi‑vendor complexity, organizational challenges, and sustainability issues, ultimately achieving continuous cost reduction and efficiency gains.
This case study details how Qunar Travel's engineering team analyzed Kafka production bottlenecks during peak traffic, added targeted monitoring, tuned thread and batch parameters, and validated the changes through gray‑scale tests, ultimately saving about 2000 CPU cores across three clusters while reducing request volume and improving network and disk utilization.
Gaode Map introduces three AI‑driven routing features—toll‑free exit recommendation, global faster‑route optimization, and high‑frequency rapid‑route detection—that combine massive traffic data, multi‑objective algorithms, and real‑time prediction to help users save both toll costs and travel time during the National Day travel peak.
This article explains how Spring Batch’s core concepts, chunk processing, metadata management, virtual‑thread integration, partitioning, and multi‑threaded step configurations can dramatically increase throughput and resource efficiency when handling large, I/O‑intensive data sets.
This article examines the exploding data volumes that pressure modern enterprises, outlines the explicit (hardware, performance) and implicit (operations, migration) costs of operating an OLAP‑based data warehouse, and explains how ByteHouse’s cloud‑native architecture reduces both cost categories while delivering real‑time analytics.
This article explains how Bilibili leverages a large‑language‑model‑driven assistant to diagnose and resolve failures and slowdowns in its massive big‑data platform, detailing the platform’s five‑layer architecture, common task issues, and the need for intelligent troubleshooting tools.
This article details the end‑to‑end design, challenges, and implementation of a cross‑cloud migration of over 200 k Hive tables and nearly 40 PB of data using the self‑developed Kirk service, covering architecture, verification steps, and lessons learned to achieve 100 % data consistency without impacting production services.
This article outlines the challenges of high‑quality data for AI, introduces Tencent Cloud’s AI‑Ready data platform with three core capabilities—DIaaS, Setats, and ES‑based knowledge search—covers the end‑to‑end WeData integration, intelligent agents for automation, and showcases ecosystem partnerships driving industry‑wide intelligent transformation.
Learn the six typical scenarios that cause data loss when writing to Paimon—ranging from checkpoint failures and misconfigured partial‑update mode to incorrect sequence fields, snapshot retention issues, concurrent bucket writes, and outdated Spark versions—and how to prevent each problem.
This article outlines Kuaishou Data Platform's mission to boost data decision efficiency, describes its three‑layer architecture, explains the BI process from data ingestion to application, and shares practical experiences and future outlook for intelligent BI powered by AI and big data.
The article examines how large‑model limitations in accuracy, explainability, and stability have stalled decision‑support use, prompting industry leaders to champion AI‑Ready data infrastructures, Data 4.0 concepts, and AI‑generated service code as the next wave of AI‑native big data platforms.
Learn four practical techniques—simple sorting, hashmap deduplication, external merge sort, and bitmap bit‑set optimization—to efficiently remove duplicate QQ numbers from a 40‑billion‑record file while staying within a strict 1 GB memory limit, even handling tighter 100 MB constraints.
This article outlines Kuaishou's Data Platform team's mission to boost data‑driven decision making through advanced compute engines, high‑performance services, and AI‑enhanced BI, detailing its architecture, challenges, solutions, and future outlook for large‑scale intelligent analytics.
When big‑data workloads hit the CPU wall, BIGO’s adoption of the open‑source Gluten project delivers native‑engine execution for Spark and a roadmap for Flink, achieving up to 30% end‑to‑end speedup, 50% memory savings, and a scalable, cost‑effective data processing platform.
This article details GoTerra's massive six‑month, 50 PB migration from GCP BigQuery to Alibaba Cloud MaxCompute, covering project scope, technical challenges such as complex data types, partition strategies, and high‑speed requirements, and explaining how the MaxCompute Migration Service (MMS) solved them with innovative architecture, scheduling, and data‑reorder techniques.
This article outlines how Kuaishou’s Data Platform Department enhances decision‑making efficiency by building advanced compute engines and high‑performance services, detailing the platform’s architecture, challenges of intelligent BI, AI‑driven solutions, and the end‑to‑end BI workflow from data ingestion to analysis.
This article explains Hive's partitioning mechanisms—including static, dynamic, and multi-level partitions—by showing how to create partitioned tables, load data with explicit or inferred partition values, compare query performance, and follow best practices for effective data organization.
This article explains why a robust tag system is essential for recommendation and mining strategies, outlines the hierarchy of entity, concept, and theme tags, and provides practical principles, architecture, and step‑by‑step methods for constructing and managing tags in large‑scale data platforms.
This article explains how ElasticSearch serves as a core middleware for large‑scale architectures, covering its role in search engines, log analysis with the ELK stack, massive data querying, and even as an independent database system, illustrated with practical examples and diagrams.
This report details a university big‑data project that built a full pipeline—from raw petrochemical market data and text mining to variable selection, XGBoost/LightGBM/Lasoo regression and RNN/LSTM/GRU models—to predict product prices across multiple horizons, evaluate errors, and deliver an interactive demo.
This case study details how Shuhe Technology, a leading fintech company, overcame data redundancy, low resource utilization, and slow reporting by adopting Alibaba Cloud EMR Serverless StarRocks for a unified, real‑time data warehouse, achieving standardized data pipelines, cost savings, and minute‑level decision latency.
This report details a university big‑data project that builds a data‑driven framework for assessing lithium‑ion battery storage health, cleaning operational data, detecting abnormal cells with DBSCAN, and predicting SOC/SOH using LightGBM, while highlighting findings, limitations, and future improvements.
This report details a university project that built a flexible, high‑performance multi‑agent large‑model framework for ecological environment big‑data analysis, covering system architecture, individual agents, memory mechanisms, report generation, a FastAPI‑LangGraph backend, a React frontend, testing methodology, and future directions.
The author shares a detailed account of their experience in the 2025 China University Big Data Competition, describing the team’s top national ranking, the shift from absolute stock price prediction to robust ranking learning, extensive feature engineering, and reflections on balancing technical ambition with real‑world constraints.
Our team "Stay Overnight" from Chongqing University of Posts and Telecommunications placed second nationally in the 2025 China University Computer Competition Big Data Challenge, navigating volatile financial data, shifting from time‑series to supervised learning, and emphasizing feature engineering to boost model performance.
This article examines the challenges of a legacy Lambda‑based membership data warehouse, introduces Apache Paimon’s lakehouse architecture and its key features, and showcases three real‑world implementations—partial‑update order wide tables, Bitmap‑based UV counting, and branch‑based data correction—while discussing benefits, remaining challenges, and future directions.
This article provides a concise overview of four major distributed storage solutions—HDFS, Ceph, FastDFS, and TFS—highlighting their architectures, strengths, weaknesses, and typical use cases for large‑scale data and e‑commerce applications.
This article details the real‑world migration of Southeast Asian tech leader GoTerra from BigQuery to MaxCompute, showcasing how the open‑source dbt‑maxcompute adapter enables smooth ELT transitions, advanced incremental strategies, performance gains, ecosystem compatibility, and comprehensive best‑practice implementations for large‑scale data pipelines.
The article explains how Kafka attains high‑throughput performance by using sequential disk writes, leveraging the OS page cache, employing producer and consumer batching with configurable parameters, and utilizing zero‑copy sendfile to minimize CPU and memory overhead, enabling stable million‑message per second rates.
The article shares a top‑5 team's experience in the 2025 China University Big Data Challenge, detailing their roster, competition rules, four key technical insights on data pitfalls, model alignment, generalization, and leveraging SOTA models, plus reflections on the event's excellent support and collaborative atmosphere.
An individual participant recounts their journey in the 2025 China University Computer Competition Big Data Challenge, detailing data cleaning, feature engineering, model building on 300‑stock historical prices, and insights gained from solo competition experience, highlighting challenges, lessons, and future directions in financial AI.
Xiaomi’s data‑lake team explains how they tackled small‑file issues, unified metadata with Gravitino, migrated Hive to Iceberg and Fileset, leveraged JuiceFS for multi‑cloud storage, and combined Iceberg and Paimon to achieve cost‑effective, high‑performance batch and real‑time analytics.
The article outlines the full lifecycle of data warehouse modeling—from business requirements and system architecture considerations through conceptual, logical, and physical design, illustrated with a procurement bidding case study and a comparison of MPP and Hadoop solutions.
Xiaomi’s engineers explain how they tackled data‑lake challenges—small files, metadata latency, and multi‑cloud costs—by combining compact storage, Gravitino‑based metadata governance, Iceberg and Paimon formats, and JuiceFS abstraction, achieving lower storage expenses, faster queries, and a roadmap toward intelligent, real‑time, multimodal lakehouses.
Faced with billions of marketing events and minute‑level decision requirements during Taobao's flash‑sale campaign, the e‑commerce data team built a real‑time lakehouse using StarRocks and Paimon, leveraged asynchronous materialized views and RoaringBitmap deduplication, and achieved sub‑second query latency, massive cost savings, and stable high‑concurrency performance.
This article explains how Baidu Netdisk transitioned from Spark Streaming to a Flink‑based Tiangong real‑time computing engine, detailing the evolution, reasons for choosing Flink, architecture, configuration examples, business use cases, technical challenges, and future platform plans.
This weekly tech roundup highlights Meituan’s dynamic container performance breakthrough, Huawei’s Mate X5 foldable adaptation, ByteDance’s Rspack 1.5 features, AI‑driven automation advances, MQTT and Crush terminal tools, Alibaba Cloud’s AI platform milestones, and practical guides for performance optimization and Chrome extension development.
JD Retail outlines the growing data management challenges it faces—including asset discovery, architecture agility, development quality, and rising IT costs—and presents a comprehensive data governance framework that leverages standards, agile architecture, development isolation, and resource optimization to improve efficiency and reduce operational expenses.
The article traces the evolution of AI hardware over the past ten years, outlining three key phases—from early chip limitations that sidelined neural networks, through CPU advances that still fell short, to the rise of GPUs and specialized AI chips that finally unlocked rapid AI deployment, while also highlighting the parallel impact of algorithmic breakthroughs and massive data growth.
This article details how a leading Southeast Asian tech group migrated its real‑time write workloads from Google BigQuery to MaxCompute using MaxCompute Streaming Insert, covering architecture, core features, migration challenges, optimization strategies, business impact, and future enhancements.
This article compares four major distributed storage solutions—HDFS, CephFS, GlusterFS, and FastDFS—detailing their architectures, strengths, weaknesses, and ideal use cases for big‑data processing, cloud-native environments, and high‑concurrency file services, and how they fit into modern infrastructure strategies.
The Auron project, originally the Blaze engine from Kuaishou, has entered the Apache Software Foundation incubator, offering a Rust‑based native vectorized execution engine that integrates with Spark, delivers over two‑fold performance gains on TPC‑DS benchmarks, and is supported by a growing open‑source community.
JD Retail faces growing data volume, redundant models, and resource‑intensive storage, prompting a comprehensive data‑governance strategy that defines standards, streamlines architecture, isolates development, and optimizes compute and storage costs, ultimately enabling more efficient, secure, and agile data operations across the enterprise.
JD Retail confronts growing data volume, redundant models, shared account risks, and rising storage costs, and responds with a comprehensive data governance framework that standardizes data, streamlines architecture, isolates development, and optimizes resources to achieve efficient, secure, and cost‑effective data operations.
This article outlines MaxCompute's decade‑long evolution—from the early PyODPS SDK to the native Distributed Python Engine—highlights the challenges big‑data platforms face in the AI era, and showcases Data+AI solutions and real‑world case studies across multimodal processing, massive text deduplication, and autonomous‑driving data pipelines.
This article details how a leading Southeast Asian tech group migrated from BigQuery to MaxCompute, designing a multi‑tenant big data platform with separate control and data planes, addressing cross‑account access, governance, and cost challenges on Alibaba Cloud.
A comprehensive list of interview topics covering self‑introduction, career moves, data‑warehouse design, team building, architecture comparisons, fact‑table classification, common dimensions, performance tuning, and data‑governance for aspiring big‑data engineers.
MaxCompute’s new Cluster Optimization Recommendation analyzes 31 days of shuffle data to automatically suggest optimal hash clustering keys, dramatically cutting shuffle traffic and CU consumption for large jobs, while providing one‑click ALTER TABLE scripts and detailed benefit reports to boost big‑data processing efficiency.
This article explores Tomoro’s Lumos data‑intelligent agent, detailing its multi‑agent architecture, technical design, practical implementations, and performance optimizations that enable seamless, low‑threshold big‑data self‑service analysis powered by AI.
After conducting 16 one‑on‑one mock interviews and debriefs, we identified five recurring issues—from lacking a holistic project view and poor expression to sloppy resume formatting, underutilizing large‑language models, and neglecting regular self‑review—that candidates should address to improve their interview performance.
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.
This article introduces the ELK stack—Elasticsearch, Logstash, and Kibana—explaining each component, their roles in large‑scale log processing, and the step‑by‑step workflow for collecting, storing, and visualizing log data in modern big‑data environments.
This article chronicles ODPS's 15‑year journey from its exploratory beginnings to a modern, AI‑enabled big data platform, detailing its four development phases, architectural layers, SQL engine upgrades, real‑time processing, lakehouse integration, and the new Data+AI capabilities offered by MaxCompute and DataWorks.
This article explains the evolution from traditional data warehouses and data lakes to the unified Lakehouse architecture, detailing its design, benefits, challenges, and research directions for delivering high‑performance SQL and advanced analytics on open‑format storage.
The article chronicles half a century of database history, analyzing the rise and collapse of systems like Informix, Sybase, FoxPro, HBase, and dBase, while examining how Oracle, Microsoft, and IBM are adapting to cloud and AI, and forecasting the forces reshaping the future of data storage.
This article explains why traditional pairwise text comparison is impractical for massive news corpora, introduces cosine similarity and SimHash as efficient deduplication techniques, walks through their mathematical foundations, step‑by‑step implementation details, code examples, and discusses trade‑offs such as accuracy versus speed.
This article examines JD.com's migration of its massive traffic data processing from a dual Lambda architecture to an integrated lakehouse solution, detailing the challenges, innovative optimizations with Flink and Hudi, performance gains, cost reductions, and future directions for real‑time data handling.
Facing growing data demands in finance, we replaced two legacy synchronization pipelines with a unified, low‑latency architecture using BabelX Real‑Time, Flink CDC, Iceberg v2 and Paimon, achieving minute‑level data freshness, ten‑to‑thirty‑fold query speedups, reduced storage costs, and streamlined schema management across multiple business units.
Over ten years, Alibaba’s data engineers transformed a modest Hadoop‑based system into a globally‑scalable, high‑performance big data platform—ODPS/MaxCompute—supporting massive offline and real‑time workloads, pioneering innovations like the 5K cluster expansion, Blink streaming, and the unified ‘Moon’ migration.
The article details how Alibaba’s Tesla SRE platform supports the massive offline and real‑time big‑data ecosystems through a layered, data‑driven operations framework—DataOps—integrating unified portals, configuration, job, workflow, and analytics platforms, enabling automated monitoring, intelligent decision‑making, and self‑healing capabilities across 100,000+ nodes.
This article details the migration of Southeast Asia's leading tech group GoTerra from Google BigQuery to Alibaba Cloud MaxCompute, explaining the performance challenges, the MaxQA accelerator architecture, optimization techniques, resource‑quota strategies, and future enhancements that together double query efficiency while reducing costs.
This article explains Spark SQL’s Catalyst optimizer, describing its extensible design, tree‑based representation, rule‑driven transformations, batch execution to a fixed point, and how Scala’s pattern matching and quasiquotes enable efficient analysis, logical optimization, physical planning, and code generation.
This article explains how to transform traditional SRE troubleshooting into a data‑driven process by pre‑collecting operational metrics into a data warehouse, using ETL to create layered data models (ODS, DIM, DWD, DWS) that enable efficient, repeatable analysis while balancing data freshness and storage costs.
The article explains how DataOps and SRE practices enable large‑scale, data‑driven operations in big‑data environments, aiming for fully automated, intelligent, and ultimately unmanned management of complex systems.
This article details how Kuaishou’s content middle platform tackled the massive challenges of real‑time, flexible, and cost‑effective data analysis at trillion‑scale by redesigning its architecture, adopting ClickHouse, splitting wide tables, and implementing a scatter‑gather execution model with pre‑shuffle and bitmap optimizations.
The article explores real‑world industrial cases—from steel furnace timing and historic lithography to modern manufacturing—showing how continuous improvement, root‑cause analysis, and careful handling of correlation versus causation can reveal hidden inefficiencies, while highlighting the limits of traditional statistics and the emerging role of AI in industrial data analytics.
Learn how to efficiently remove duplicates from 4 billion QQ numbers using a memory‑friendly Bitmap approach that fits within a 1 GB limit, including calculations, step‑by‑step implementation, Java code, and a discussion of its advantages and drawbacks.
In his 2025 World Internet Conference Digital Silk Road Forum keynote, Zhou Hongyi warned that the programmable, AI‑driven, data‑centric world amplifies cyber vulnerabilities, described the rise of state‑level cyber warfare and AI‑powered attacks, and outlined 360’s security‑as‑service strategy and global cooperation plans to protect nations and enterprises.
This article details the real‑world migration of a leading Southeast Asian tech group from BigQuery to MaxCompute, exposing the three major challenges, the data‑driven performance‑optimization methodology, and the concrete techniques—Auto Partition, UNNEST redesign, large‑query graph optimizations, and intelligent tuning—that delivered dramatic cost reductions and query‑speed gains.
This article introduces the comprehensive design and evolution of a unified metadata lineage platform for big data, covering background, data processing chain, lineage models, system architecture, quality metrics, application scenarios, and future plans to enhance data governance, quality, and security.
The paper “Noise Matters: Cross Contrastive Learning for Flink Anomaly Detection”, accepted at VLDB 2025, introduces a novel cross‑contrastive method that leverages attention‑based representations and a boundary‑aware loss to detect Flink‑specific hotspot anomalies, achieving a 12.1% F1 improvement over state‑of‑the‑art techniques.
This article summarizes the major Apache Flink 2.0 updates released in the first half of 2025, covering architecture separation, cloud‑native deployment, AI‑driven agents, SQL enhancements, data integration, operational tools, and performance optimizations for real‑time intelligent computing.
This article outlines the overall architecture, data architecture, logical project structure, and the construction of a data resource center for a data lake and application platform, illustrated through a series of diagrams that depict each component and their interconnections.
Prof. Dou Dejing shares his journey into Fudan University's Data Intelligence Lab, outlines the history and synergy of big data and AI, reviews generative AI breakthroughs, evaluates large‑model strengths and weaknesses, and explores their expanding industrial applications and market potential.
This article explores how ClickHouse is deployed across WeChat for real‑time analytics, introduces a suite of performance‑monitoring tools, details lakehouse read and bitmap optimizations, and describes the integration of AI‑driven vector search, showcasing substantial speedups and scalability improvements.
The article examines a real-world migration of a major Southeast Asian tech group from Google BigQuery to Alibaba Cloud MaxCompute, highlighting the challenges of complex data types, the columnar storage and execution engine redesigns, and the resulting performance gains that often surpass BigQuery.
This article explains how Youzan transformed its Kubernetes clusters from static over‑commit scheduling to load‑balanced mixed workloads using Koordinator and the Longxi kernel, achieving higher CPU utilization, lower costs, and better resource management for both online and offline services.
This article presents a curated collection of cutting‑edge data lake and lakehouse case studies—including real‑time analytics, cloud‑native architectures, industry implementations from sales platforms to automotive IoT, and the latest advancements in open‑source projects—offering insights into modern big‑data strategies and governance.
This article reviews the most effective Flink optimization techniques since 2022, including operator‑level TTL, mini‑batch processing, two‑phase aggregation, multi‑dimensional DISTINCT with FILTER, lookup join caching strategies, and TopN implementations, each rated with recommendation stars for production use.
This article details the complex migration of a leading Southeast Asian tech group's data warehouse from Google BigQuery to Alibaba Cloud MaxCompute, outlining challenges such as storage format differences, SQL compatibility, and performance tuning, and explains how the new Append DeltaTable format with dynamic bucketing and incremental reclustering resolves these issues.
The article examines the June 2025 TIOBE ranking where SQL fell to its lowest position, recounts its historical highs and removal from the list, highlights everyday reliance on SQL, and analyzes the growing prominence of NoSQL and other programming languages in the era of AI and big data.
This article examines Alibaba's extensive cloud‑native technology stack—including distributed computing, storage, middleware, real‑time data processing, AI platforms, performance engineering, and security—revealing how its architects design systems that handle massive transaction volumes during events like Double 11.
The article explores why architects at leading tech firms command six‑figure salaries while those in traditional companies earn far less, highlighting gaps in technical depth, massive data handling, performance optimization, business insight, continuous learning, and the scarcity of true senior architects.
The article explains how relying solely on historical data for prediction often leads to large errors because future structural changes and missing variables are ignored, and it proposes causal modeling, scenario simulation, and real‑time signals as more reliable forecasting approaches.
The article reviews key topics from the FFA2025 Singapore conference, highlighting Flink’s new AI‑focused Agents framework, the breakthrough Flink 2.0 disaggregated state architecture, emerging lake storage solutions like Paimon, and the Fluss streaming table store, illustrating how big‑data platforms are evolving for AI workloads.
This article presents a curated list of sessions covering cloud Lakehouse technology for real-time, multidimensional data analysis, including case studies from SalesEasy, Changan Auto, Tencent, and JD, as well as discussions on data lake adoption, streaming lake Paimon, and the relevance of metadata‑driven data governance in the digital economy.
This article presents a curated list of case studies and insights on cloud Lakehouse technology, covering real-time intelligent analytics, data architecture simplification, IoT big‑data platforms, integrated data platforms, and the evolving role of metadata‑driven data governance in the digital economy.
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.
