This article explains the evolution from traditional data warehouses to modern lakehouse architectures, introduces the Arctic system’s dynamic hash tree for fast update/delete, describes file splitting with sequence/offset ordering, and compares copy‑on‑write versus merge‑on‑read techniques for achieving low‑latency analytics.
This article shares practical notes on building a one‑stop big data platform, outlining essential functions such as data extraction, cleaning, storage, analysis, governance, and security, and presents implementation case studies from WeBank, Beike, and iQIYI to illustrate real‑world architectures and solutions.
Data warehouses store structured data centrally for reporting and analysis, while data lakes retain raw data in various formats, offering flexible, low‑cost, schema‑on‑read processing; the article explains their definitions, key differences, common misconceptions, and why many organizations now combine both to enable self‑service big‑data analytics.
This article presents a technical deep‑dive into Bilibili’s evolution from offline to real‑time data processing, describing the challenges of timeliness, ETL, AI feature engineering, and the design of a Flink‑on‑YARN incremental pipeline that supports trillion‑scale message throughput and AI‑driven real‑time applications.
The article describes a real‑time user behavior analysis platform built on a ClickHouse cluster, detailing its architecture, Hive‑to‑ClickHouse data ingestion with user‑ID routing, table designs for behavior and group data, and five analytical methods—event, funnel, path, retention, and attribution—leveraging shard‑level parallelism and custom functions for high efficiency.
In a keynote at the 2021 iQIYI World Conference, the CTO outlined how AI, big data, and cloud computing power three intelligent production suites, interactive user features, and immersive XR live concerts, illustrating the company’s tech‑driven strategy to reshape entertainment creation and consumption.
The article details how Alibaba’s Data Bank leverages AnalyticDB’s cold‑hot tiered storage, high‑throughput real‑time writes, and low‑latency OLAP capabilities to handle petabyte‑scale consumer data, support flexible AIPL analysis, crowd profiling, and rapid audience selection while cutting costs and ensuring elasticity during peak events.
The article offers a comprehensive technical deep‑dive into ByteDance’s home‑grown distributed graph database and graph‑processing engine, ByteGraph, covering its directed‑property graph model, Gremlin query support, multi‑layer architecture, storage strategies for massive data, and real‑world graph‑computing practices.
The article details the development, challenges, and redesign of JD.com’s self‑operated rebate system, describing its early monolithic architecture, data‑intensive processing pipeline, migration to a modular, high‑availability platform built on Spark, Hive, and Elasticsearch, and the resulting performance and operational improvements.
The article describes how Yuanfudao's data middle platform built a high‑performance OLAP service using the MPP HOLAP engine DorisDB to unify real‑time and batch analytics, meet low‑latency and high‑concurrency requirements, and support diverse education‑industry use cases such as live‑stream monitoring, advertising, and order analytics.
The 2021 Tencent Advertising Algorithm Competition featured live streams on May 10-12, 2021, with experts discussing the competition's technical aspects and practical applications of the Angel distributed machine learning framework.
The article explains how China’s seventh national census, covering 1.41 billion people, was conducted using fully electronic data collection, self‑service mini‑programs, massive cloud‑native infrastructure, and high‑performance databases to achieve real‑time processing and unprecedented scale.
Yuanfudao's data middle platform leverages the MPP database DorisDB to create a unified OLAP system that supports both real‑time and batch analytics, handling millions of queries daily with sub‑second latency while meeting complex business requirements across its education services.
This article details Baixin Bank's construction of a Flink‑driven real‑time computing platform integrated with Hudi as a real‑time data lake, covering background, architecture, data collection, transformation, storage layers, technical challenges, future roadmap, and practical lessons for similar big‑data initiatives.
The article explains the importance of data quality in the big data era, defines key quality dimensions such as completeness, uniqueness, validity, consistency, accuracy, timeliness, and credibility, and details how each dimension can be measured and enforced through specific constraints and validation rules.
Fluid is an open‑source cloud‑native data orchestration and acceleration system that runs on Kubernetes, offering storage‑agnostic datasets, distributed caching, intelligent scheduling, and performance optimizations for data‑intensive AI and big‑data workloads.
This article explains the fundamental distinctions between data lakes and data warehouses, outlines five critical differences—including data retention, type support, user support, adaptability, and insight speed—and offers guidance on selecting the appropriate solution based on organizational needs and technology options.
This article provides a detailed introduction to data middle platform concepts, covering data aggregation, ingestion tools, offline and real‑time development, data governance, service layers, monitoring, and deployment patterns, illustrating how enterprises build unified data ecosystems across various industries.
This case study details how integrating JuiceFS with Presto reduced HDFS cluster load by about 26%, achieved over 90% cache hit rate for ad‑hoc queries, and lowered average query latency by roughly 13%, while simplifying operations and improving system stability.
AWS introduces a new EMR Studio IDE to accelerate data science workflows, while the Linux community bans University of Minnesota contributions over malicious patches and Google Chrome adopts Intel‑Microsoft hardware‑enforced stack protection to harden browser security.
This article explains how JD constructs its OLAP platform from data ingestion to storage, querying, and management, describing the diverse data sources, real‑time and offline processing, scalability, consistency, fault tolerance, and future optimization plans, while addressing key technical challenges and solutions.
This article presents the background, requirements, architectural design, component interaction, and implementation details of AutoHome's real‑time data transmission platform built on Apache Flink, highlighting its high availability, exactly‑once semantics, scalability, DDL handling, and integration with existing streaming services.
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 introduces the GoPUP library, which provides over a hundred free data interfaces—including social media indexes, macro‑economic figures, company information, and epidemic statistics—accessible with simple Python code, making data analysis faster and easier.
This article presents Kuaishou's comprehensive engineering practices for improving Flink's stability, task startup latency, and SQL performance, including high‑availability Kafka connectors, fault‑recovery mechanisms, I/O reductions, asynchronous job upgrades, aggregation optimizations, and future resource‑utilization plans.
The article explains the concept of a data middle platform, its role in integrating and centralizing enterprise data, the drivers behind its adoption, architectural layers, implementation challenges, market landscape, and real‑world case studies, highlighting how big‑data, cloud and AI technologies enable digital transformation.
This article provides a detailed overview of the ELK stack—including Elasticsearch, Logstash, Kibana, and Beats—explaining its components, why to use it for centralized log management, various deployment architectures, system tuning, security setup, and step‑by‑step installation and configuration commands for a production‑grade environment.
Kafka 2.8.0, released on April 19, 2021, introduces the groundbreaking Raft Metadata mode that eliminates the need for ZooKeeper, alongside numerous new features, bug fixes, and enhancements such as API controls for stream threads, SASL_SSL mutual TLS, and IP rate limiting.
The inaugural Techo TVP Developer Summit in Shenzhen gathered over 500 developers to explore the latest trends in databases, distributed systems, big data, and cloud‑native technologies, offering expert analyses, real‑world case studies, and career guidance for data professionals.
This article provides a comprehensive introduction to the ELK stack—including component descriptions, architectural diagrams, reasons for adoption, and step‑by‑step installation and configuration of Filebeat, Logstash, Elasticsearch, and Kibana on Linux, with optional Kafka integration for advanced pipelines.
This article explains how NVIDIA's RAPIDS Accelerator leverages GPUs to speed up Apache Spark 3.0 workloads, detailing the underlying architecture, benchmark results on TPC‑DS and recommendation models, required configuration changes, supported operators, shuffle optimizations, and the enhancements introduced in versions 0.2 and 0.3.
This guide walks through preparing three Linux servers, installing JDK 1.8, configuring Hadoop core, HDFS, MapReduce, and YARN XML files, setting Java environment variables, formatting HDFS, and starting all services to access the Hadoop web UI.
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 article explains the motivations behind Apache Iceberg, its design principles such as snapshot and MVCC, compares it with Hive, and describes how NetEase Cloud Music adopted Iceberg to improve metadata handling, query performance, and operational stability for massive daily log data.
The Tencent Ad Algorithm Competition, now in its fourth edition, has gained significant academic recognition by aligning with the ACM MM Grand Challenge, introducing new tracks in video advertising technology to address multimedia challenges in the 5G era.
This article details Zhihu’s transition from a Sqoop‑driven data integration system to a Flink‑centric platform, covering business scenarios, historical architecture, design goals, technology choices, performance optimizations, and future plans for unified streaming‑batch processing across diverse storage systems.
This article provides an in‑depth technical overview of Toutiao’s rapid growth, data collection pipelines, user modeling, cold‑start strategies, recommendation engine architecture, storage solutions, push notification system, microservice design, and its three‑layer PaaS platform, illustrating how the news app serves hundreds of millions of users daily.
Apache Kafka 2.8.0 introduces several significant enhancements, including a new group API, mutual TLS authentication for SASL_SSL listeners, JSON request/response logging, broker connection rate limiting, topic identifiers, self‑managed quorum replacing ZooKeeper, and numerous improvements to Streams and Connect APIs for more reliable real‑time data pipelines.
By classifying data streams, aggregating large‑scale T+1 records in six‑hour windows, encoding attributes with multi‑value mappings, storing compressed rule‑hit backups, and synchronizing recall tables in real time, Mahé’s selection‑and‑delivery pipeline cut end‑to‑end latency from minutes to seconds, achieving robust second‑level responsiveness.
This article debunks eight common misconceptions about data lakes, explains why they are not mutually exclusive with data warehouses, clarifies that they are not limited to Hadoop or raw data only, and provides practical tips for building flexible, secure, and business‑driven data lake solutions.
This article presents a comprehensive construction plan for an Industrial Internet big data platform, detailing its overall architecture, data acquisition, edge processing, cloud storage, analytics, security measures, and deployment best practices to enable scalable and reliable industrial IoT solutions.
This article explains how to construct near real‑time Elasticsearch indexes for petabyte‑level datasets by comparing MySQL limitations, describing Elasticsearch fundamentals, and detailing a pipeline that uses Hive, wide tables, MySQL binlog, Canal, and Otter to achieve second‑level index updates.
This article introduces space-filling curves such as Z‑ordering, Hilbert, and XZ‑Ordering, explaining their mapping algorithms and how they transform multidimensional spatial data into one‑dimensional indices for efficient storage and querying in key‑value databases, while discussing challenges and practical examples.
This article reviews the evolution of Beike's OLAP platform—from the early Hive‑to‑MySQL stage, through a Kylin‑based architecture, to a flexible multi‑engine solution—detailing the design choices, metric system, engine selection criteria, encountered challenges, and future development plans.
Meituan’s hotel‑travel platform tackled exploding data‑quality, cost, efficiency, and security issues by establishing a full‑link governance framework—standardized processes, a Data Management Committee, and unified “One Model, One Logic, One Service, One Portal” systems—that cut per‑unit costs by ~40%, boosted engineer productivity over 60%, eliminated major security incidents, and set the stage for autonomous, AI‑driven data governance.
On April 15, Tsinghua University's Computer Science Department and TAL Education's Joint Research Center inaugurated Phase II of their partnership to advance intelligent education through AI-driven teaching environments, interactive mechanisms, knowledge‑graph construction, and personalized assessment technologies.
This guide compiles practical Spark tuning techniques, covering essential configuration parameters, programming best‑practices, detailed shuffle mechanics, and join optimization strategies, while also addressing common errors and mitigation steps, enabling developers to improve performance and resource utilization in large‑scale data processing jobs.
This article explains HDFS’s master‑slave architecture, detailing the roles of NameNode and DataNode, namespace management, communication protocols, client functions, common configuration parameters, maintenance commands, and the inherent limitations of a single‑NameNode design.
This article provides a comprehensive overview of HDFS—including its design goals, core components, data read/write workflows, high‑availability mechanisms, federation, storage policies, colocation benefits, and practical usage scenarios—explaining why it is the foundational distributed file system for large‑scale data processing.
The article explains what the digital economy is, its relationship with digital transformation, the strategic importance placed on it by China's 14th Five‑Year Plan, and offers guidance for IT professionals on how to respond to this emerging national priority.
This article explains how iQIYI’s data middle platform addresses the rapid growth and challenges of big data by providing a unified, standardized, and service‑oriented architecture that includes data production, processing, governance, metadata, AI‑enhanced services, and a roadmap for future enhancements.
This article provides a comprehensive overview of Toutiao's rapid growth, massive user base, data collection pipelines, user modeling, recommendation engine, storage solutions, message push strategies, micro‑service architecture, and virtualization PaaS platform, illustrating how big‑data technologies enable personalized news delivery at scale.
This article explains the small‑file problem in Spark SQL on HDFS, its impact on NameNode memory and query performance, describes how dynamic partition inserts and shuffle settings generate many files, and presents practical solutions such as partition‑based distribution, random bucketing and adaptive query execution to control file count.
This tutorial explains how to select a technical architecture, design a three‑layer data warehouse (ODS, CDM, ADS), model tables and dimensions, choose storage strategies, handle slowly changing dimensions, synchronize data with DataWorks, and implement dimensional modeling and fact tables using Alibaba MaxCompute for big‑data analytics.
The article reviews the current state of offline Hive‑based data warehouses, explains the emergence of real‑time data warehouses (1.0) built on Kafka and Flink, discusses their limitations, and outlines the progression toward batch‑stream unified architectures (2.0 and 3.0) leveraging data‑lake technologies such as Iceberg.
This article explains six key user‑behavior analysis methods—event analysis, retention analysis, distribution analysis, conversion‑funnel analysis, path analysis, and session analysis—showing how they help businesses understand user actions, optimize product design, improve conversion rates, and boost revenue through data‑driven insights.
This article explains advanced Spark performance tuning techniques, focusing on diagnosing and resolving data skew and shuffle bottlenecks through stage analysis, key distribution inspection, and a variety of practical solutions such as Hive pre‑processing, key filtering, parallelism increase, two‑stage aggregation, map‑join, and combined strategies, while also covering ShuffleManager internals and related configuration parameters.
This article provides a comprehensive guide to Spark performance optimization, covering development‑level tuning principles, resource configuration parameters, practical code examples, and best‑practice recommendations to achieve high‑throughput big‑data processing.
This article explains how the open‑source Fluid project addresses the inefficiencies of data‑intensive AI and big‑data workloads in cloud‑native Kubernetes environments by introducing a data‑centric abstraction, dual orchestration mechanisms, and seamless integration with Alluxio to achieve faster, secure, and scalable data access.
This article describes how Ctrip Finance built a unified financial data center by collecting MySQL binlog streams with Canal, transporting them via Kafka, persisting to HDFS with Spark‑Streaming, and merging into Hive tables, while addressing performance, idempotency, delete handling, and data‑quality checks.
This article details Beike's large‑scale OLAP platform, explaining why Druid was chosen over Kylin, describing the platform's four‑layer architecture, presenting performance and storage benchmarks, and outlining practical improvements to data ingestion, real‑time distinct counting, and cluster stability for high‑concurrency business scenarios.
This comprehensive guide introduces Kafka as a high‑throughput, distributed, publish‑subscribe messaging system, detailing its core concepts, architecture, features, replication, log management, reliability guarantees, and typical use cases such as log collection, real‑time analytics, and cross‑cluster mirroring.
This article presents an in‑depth technical overview of Kuaishou's massive HDFS deployment, detailing its architecture, petabyte‑scale data and thousands‑of‑node clusters, the key scalability challenges faced, and the custom solutions—including FixedOrder, RBF balancer, observer read, slow‑node mitigation, and tiered protection—implemented to keep the system performant and reliable.
The article describes how algorithm testing teams tackled data‑quality and interface‑semantic monitoring problems by building a unified business monitoring platform that checks table, storage and service consistency, validates response semantics, and, through dashboards, alerts and correction tools, quickly identified dozens of offline and online issues, guiding future reliability enhancements.
iQIYI transformed its data infrastructure from a traditional offline T+1 model to a comprehensive real‑time ecosystem—leveraging Kafka, Flink, a three‑layer Stream Data Service Platform, the Talos drag‑and‑drop pipeline, and a Druid‑based analytics platform—to enable low‑latency monitoring, personalized recommendations, ad targeting, and continuous machine‑learning workflows while planning future stream‑batch integration and lake‑warehouse convergence.
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 examines how policy‑driven industrial internet initiatives, combined with data‑rich C2M models and AIoT integration, are reshaping manufacturing in China, highlighting Suning's smart‑fridge case, strategic partnerships, and the broader push toward a digital‑first industrial era.
Suning’s data middle platform integrates an accelerated OLAP engine, a star‑schema metrics system, a standardized visualization tool, and a unified report portal to break data silos, enhance security, and transform traditional enterprises into technology‑driven businesses.
This article details how KuJiaLe's big data team replaced their legacy ADB and Presto clusters with a DorisDB MPP database, achieving sub‑second query latency, unified real‑time and offline analytics, simplified ETL pipelines, and significant cost savings while supporting billion‑row tables and high‑QPS workloads.
This article traces the history of big‑data computing engines—from early MapReduce and Hadoop through Spark, Storm, Flink, and the newer Ray—explaining their technical advances, real‑world applications in AI and finance, and why graduates should consider a career in this rapidly evolving field.
This article presents ByteDance's recent Flink enhancements, detailing a single‑point recovery mechanism for the network layer and a regional checkpoint strategy that together improve failover latency, reduce output loss, and enable scalable, high‑throughput stream processing for large‑scale real‑time recommendation workloads.
This article explains how to configure Elasticsearch nodes for hot and cold data, assign special node attributes, adjust index templates, and use API calls to migrate data, demonstrating significant query speed improvements through real‑world performance tests.
This article details how a bank transformed its retail system data pipeline from a monolithic DB2 setup to a distributed Oracle‑Hadoop architecture, evaluated five extraction tools, selected Sqoop direct mode, and implemented customizations to achieve over 50% performance gains and reliable incremental data capture.
This article explores Alibaba Cloud's Feitian big data platform and its front‑end intelligent solutions—covering smart editors, code recommendation, code diagnostics, automated visualization, and algorithm engineering—to illustrate how AI enhances developer productivity and product intelligence.
This article presents a graph‑neural‑network approach to predict user influence, cluster users with distributed Louvain methods, and conduct network‑aware A/B experiments that accurately evaluate large‑scale marketing campaigns despite strong network effects.
Suning Carrefour’s rapid digital overhaul since joining Suning in 2019 showcases how AI, big data, and omni‑channel strategies can boost store efficiency, reshape business models, integrate supply chains, and drive high‑growth retail performance.
Idlefish’s new multi‑engine concurrent search architecture replaces the tightly‑coupled single‑engine pipeline with deep engine isolation, asynchronous multi‑engine recall, and unified result merging, cutting dump build time from 14 h to 5 h, shrinking memory use dramatically, improving latency by only ~15 ms, and boosting exposure by 50 % and orders by 33 %.
This article explains the principles and implementation of Simhash, covering the shortcomings of traditional hash functions, the use of cosine similarity, random projection for dimensionality reduction, locality‑sensitive hashing, and practical optimizations for large‑scale duplicate detection.
This article explains how Kuaishou’s massive offline compute clusters—tens of thousands of machines processing hundreds of petabytes daily—are managed by a heavily customized YARN stack and the home‑grown Kwai Scheduler, detailing architecture, scheduler evolution, multi‑scenario optimizations, and future scaling plans.
This developer community weekly roundup highlights CCTV's new big‑data governance platform, RedMonk's programming language rankings, Chromium‑based browsers adopting a four‑week release cycle, PyTorch 1.8 with AMD support, the world’s first AI‑driven earthquake monitoring system, Red Hat OpenShift 4.7, a deep meta‑learning model for city sales prediction, and a CVPR breakthrough in controllable human image generation.
This article shares ten practical pitfalls encountered while moving hour‑level Spark session processing jobs to Apache Flink, covering parallelism skew, state TTL, checkpoint handling, logging, debugging, state migration, Reduce vs Process, input validation, event‑time handling, and the trade‑offs of storing data inside Flink.
Given two files each containing 5 billion 64‑byte URLs (≈320 GB total) and only 4 GB of memory, the solution partitions the URLs by hash modulo 1000 into 1,000 smaller files, then uses hash sets to identify the intersecting URLs efficiently.
This article demystifies quantitative investing by explaining its basic concepts, common strategies, historical growth, inherent limitations, and the role of AI and big data, while urging investors to view quant methods as tools rather than a universal solution.
At the 2021 National Retail CIO Conference in Shanghai, Suning’s Director Wang Junjie detailed the company’s AI, big‑data and cloud‑based three‑step digital transformation strategy, its suite of five mature digital products, and its call for partners to extend these solutions across industries.
The article details the design and engineering of a high‑performance path analysis model for internet data operations, explaining session handling, Sankey visualizations, adjacency‑table storage, multi‑granular session partitioning, Spark‑to‑ClickHouse pipelines, and optimizations that enable billion‑scale user‑path queries in about one second.
At Kuaishou, the Hive MetaStore service, which stores metadata for Hive, faced scalability and performance challenges due to massive dynamic partitions and high query volume, leading to a series of architectural optimizations—including read‑write separation, API enhancements, traffic control, and federation—to improve stability and efficiency.
The article analyzes the challenges of moving traditional on‑premise big‑data platforms to the cloud, outlines the cost‑saving opportunities of cloud‑native data lakes, presents three core architectural principles, and reviews Tencent Cloud's data lake product suite and its key use cases.
This week’s developer roundup covers Google’s Flutter 2 launch, JD Cloud’s next‑gen server, Apache Flink 1.12.2 bug‑fix release, sidewalk robots classified as pedestrians, Microsoft Mesh mixed‑reality platform, Facebook’s self‑supervised SEER model, plus recent AI research from EMNLP and COLING conferences.
This article explains the architecture and core concepts of Elasticsearch and Lucene, outlines the requirements for cross‑month and high‑speed queries on massive datasets, and provides detailed index and search performance tuning techniques—including bulk writes, shard routing, doc‑values management, and pagination strategies—to achieve sub‑second response times on billions of records.
This article explores how data clustering techniques such as linear order, Z‑order, and Hilbert‑curve ordering can be applied in Apache Spark and Apache Iceberg to achieve efficient data skipping on terabyte‑scale tables, dramatically reducing file scans and enabling sub‑second interactive analytics for multi‑dimensional queries.
The article examines China's push for free public data sharing, highlighting policy directives, the need for top‑level design, security standards, and education to create a unified, safe data‑governance framework that fuels the digital economy.
Suning’s Data Middle Platform integrates an accelerated OLAP engine, a star‑schema metric system, a visualization tool built on standardized dimensions, and a unified report portal to solve data silos, improve security, and enable enterprises to evolve into technology‑driven organizations.
Apache Beam 2.28.0 introduces extensive Parquet support, new hash functions in BeamSQL and ZetaSQL, ApproximateDistinct via HLL, enhanced I/O connectors including SpannerIO for Numeric fields, ParquetIO schema support, KafkaTableProvider thrift, HadoopFormatIO key/value cloning skip, and various other improvements.
This article details how Youzan's offline Spark computing platform was transformed for the cloud‑native era by migrating from YARN to Kubernetes, introducing containerization, storage‑compute separation, dynamic allocation, deployment optimizations, and a collection of practical lessons to reduce cost and improve resource utilization.
This article provides a comprehensive overview of ClickHouse, covering its background, core features, columnar storage, vectorized execution engine, table engines, distributed architecture, performance benchmarks, real‑world deployment at TAL Education, monitoring practices, encountered challenges, and future planning.
This article explains the fundamentals of parallel computing in the big‑data era, compares parallelism and concurrency, outlines GPU and distributed‑computing solutions, and provides a detailed guide to Python’s multiprocessing module with code examples, performance tests, and practical tips.
This article outlines Meituan's hotel‑travel data governance evolution, describing the key quality, cost, security, standardization and efficiency challenges faced as the business scaled, and detailing the organizational, technical, metric, service and product‑entry solutions implemented to achieve systematic, measurable, and automated data governance.
This article explores practical methods for optimizing Flink real‑time task resources on Kubernetes, focusing on memory usage analysis via GC logs and message‑processing capacity assessment, proposing automated detection of over‑provisioned memory and CPU, and outlining a workflow for resource adjustment to reduce costs.
This article details JD Search's journey from a Storm‑based pipeline to a Flink‑driven architecture backed by Apache Doris, covering business requirements, technical challenges, design trade‑offs, performance optimizations for massive traffic spikes, and future plans for their real‑time OLAP data warehouse.
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.
