At Yunqi 2026, Alibaba Cloud unveiled its Agentic Lake + Agentic Streaming architecture, combining DLF, Flink, EMR Ray/Daft, StarRocks, and Milvus to provide a unified multimodal data infrastructure for AI agents in autonomous driving, embodied intelligence, and enterprise operations.
This article explores data quality challenges in real-time data warehouses, covering timing agreements for late-arriving data, reconciliation techniques using Flink and Paimon, SQL-based checking strategies, alerting with actionable details, and iterative quality validation processes.
This article details the end-to-end architecture of a real-time data warehouse, covering data ingestion via CDC and Kafka, stream processing challenges like deduplication and event-time handling, layered modeling (ODS/DWD/DWS/ADS), unified metric definitions, and critical production monitoring for latency, lag, data reconciliation, and consistency with offline results.
This article details how to build a production-ready real-time data warehouse using Flink, covering CDC ingestion, event-time processing, dimension joins, upsert aggregation, state TTL, checkpoint tuning, exactly-once verification, and a pre-launch checklist for reliability.
The article explains why real‑time data‑warehouse projects often fail in production and presents a complete, production‑ready solution that layers ODS, DWD, DWS and ADS using Flink CDC to capture MySQL changes, Kafka for buffering and replay, and Doris for OLAP storage, with detailed guidance on architecture, state handling, fault‑tolerance and operations.
Apache Paimon combines a lakehouse table format with an LSM-tree update engine to handle continuous updates, historical snapshots, and multi-engine access in real-time data warehouses, reducing the need for separate Kafka, Hive, and OLAP systems.
The 17th China Database Technology Conference showcased DBA‑AI transformation strategies, highlighting five‑stage evolution paths, lightweight big‑data + AI architectures, and the emerging Agentic Data Stack, while warning of AI hallucination risks and emphasizing human‑AI collaboration for future data engineering.
The article details how a live‑streaming data team built an AI‑assisted, metric‑driven system that lets users describe a real‑time data task in under 200 characters, automatically generates DSL and Flink SQL, and supports safe incremental adjustments via a Hook protocol, cutting development time from days to minutes.
This article deeply compares two StarRocks real-time wide table architectures—direct primary key table writes versus Paimon lakehouse external tables—across seven dimensions including query performance, write latency, data reuse, resource isolation, operational complexity, storage cost, and evolution potential, providing banking scenario selection criteria and a recommended Flink+Fluss+Paimon+StarRocks streaming lakehouse architecture.
Apache Fluss graduates to a top-level project, introducing the Lakestream architecture that unifies real-time streaming and historical lakehouse storage through tiered hot/cold storage, unified metadata, and Union Read, eliminating Lambda architecture complexity and enabling sub-second analytics on a single logical table.
This guide compares Doris, ClickHouse, and StarRocks for billion‑row real‑time analytics, outlining their strengths and weaknesses across write throughput, update handling, join performance, concurrency, cost, and operational complexity, and provides concrete modeling, ingestion, query governance, and fault‑tolerance recommendations to help teams choose the most suitable OLAP platform.
The DSP advertising data pipeline was re‑architected by splitting three distinct links—NRR, BT, and CT—and assigning DLF Paimon for massive low‑frequency data, EMR Serverless StarRocks primary tables for high‑value low‑latency queries, and unified service‑layer merging, achieving 2‑minute freshness, sub‑5 ms point queries, ~60% storage cost reduction, and improved fault isolation.
This lesson explains why a single database cannot scale for massive e‑commerce data, introduces the three core pillars of distributed storage—sharding, replication, and horizontal scaling—covers batch and stream processing differences with Spark, Hive, Flink and Storm, compares compute‑storage integration versus separation, and shows how Kafka, HDFS, Spark and Flink fit together in a real‑time data platform.
This article provides a detailed, structured survey of the most widely used open‑source big‑data technologies—including Hadoop ecosystems, storage systems, processing engines, query tools, data ingestion, exchange, messaging, scheduling, governance, visualization, mining, and cloud platforms—highlighting each project's origins, core features, typical use cases, and notable strengths or limitations to aid technology selection and system design.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based stack to a Lambda‑enhanced architecture and finally to a Lakehouse with incremental compute, cutting architecture complexity, resource and development costs by two‑thirds while delivering second‑level analytics on petabyte‑scale data.
The article walks through an interview scenario where a candidate must handle a 10 GB CSV using just 512 MB of memory, critiques naive answers, and presents a step‑by‑step streaming, chunked, and checkpoint‑based solution with optional Kafka/Flink considerations.
The article walks through constructing a full‑stack real‑time recommendation system—from user‑behavior collection via Kafka, through Flink streaming jobs for hot‑list, user and item profiling, to storage in Redis, HBase and Elasticsearch, and finally a React/Ant Design console that visualizes the pipeline and enables debugging.
At Flink Forward Asia 2026, experts argued that real‑time computing is undergoing a fundamental identity shift: Flink is evolving from a batch‑stream engine into the core infrastructure for AI agents, driven by data gravity, agentic streaming, GPU acceleration, and a unified data lake.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based ad‑hoc analysis system to a Lambda‑style architecture and finally to a lakehouse with incremental compute, cutting architecture complexity, resource and development costs by one‑third while delivering second‑level queries over petabyte‑scale data.
A rare silent data‑corruption bug in JDK 25’s G1GC caused Parquet and ORC files written by Spark and Flink to become unreadable, prompting a multi‑stage investigation that traced the issue to an optional evacuation flaw affecting JNI‑pinned objects, which was later back‑ported and fixed in the OpenJDK community.
Bailei Knowledge Base delivers an enterprise‑grade, full‑modal Retrieval‑Augmented Generation solution covering documents, tables, images and audio‑video, powered by Flink's high‑throughput streaming for billions of daily document indexes and DLF/Paimon’s three‑layer reliable backup, achieving sub‑200 ms latency and 99.99% availability.
Xiaohongshu, with over 3.5 billion monthly users and daily logs in the trillions, migrated 500 PB of data to Alibaba Cloud and iterated its data platform through four architecture generations—ClickHouse‑based ad‑hoc, Lambda, Lakehouse, and a unified incremental compute model—cutting resource, development, and storage costs to one‑third while delivering sub‑10‑second query latency at petabyte scale.
The article explains why small‑to‑medium teams need a unified web‑based management console for Flink + Paimon real‑time data warehouses, outlines the operational pain points of current script‑driven workflows, and details the proposed solution’s core modules, tech stack, and role‑based access design.
The article walks through the architecture and core workflow of a lightweight real‑time data warehouse built on Flink and Paimon, covering service‑layer refactoring, CDC task lifecycle, dual Flink submission modes, metadata synchronization, and layered security measures for small‑to‑medium enterprises.
This article presents an end‑to‑end design of a production‑grade observability platform that ingests millions of real‑time logs, metrics, and events, detailing functional and non‑functional requirements, capacity planning, component choices such as Kafka, Flink, Elasticsearch, object‑storage data lakes, and the trade‑offs involved.
Xiaohongshu, with over 350 million monthly users and daily logs in the billions, migrated its data platform from AWS to Alibaba Cloud and iterated four times—from a ClickHouse‑based ad‑hoc layer to a Lambda architecture and finally a Lakehouse with incremental compute—cutting architecture complexity, resource cost and development effort each to about one‑third while delivering second‑level analytics on trillion‑scale data.
The article details Xiaohongshu's step‑by‑step migration from a simple ClickHouse‑based analytics stack to a Lambda‑style 2.0 architecture and finally to a Lakehouse‑based 3.0 design, highlighting concrete performance numbers, cost reductions, and the definition of a generic incremental‑compute model (SPOT) that underpins the evolution.
This article details a real‑time risk control system for e‑commerce and finance built on Apache Flink 2.2.1 and CEP, featuring a dynamic Aviator rule engine, three Kafka event streams, multi‑channel output to Redis, MySQL and Kafka, a Spring Boot‑React management UI, and step‑by‑step deployment instructions.
This article walks through setting up the foundational environment for a real‑time data visualization pipeline using RocketMQ, Flink, Elasticsearch and Kibana, covering code configuration, index verification, Kibana dashboard creation, and practical production tips.
This article walks through setting up a real‑time dashboard stack using RocketMQ, Flink, Elasticsearch and Kibana, covering environment preparation, order data simulation, Flink job creation with source, transformation and Elasticsearch upsert sink, and visualisation considerations.
This article details Qichacha's use of large language models to extract field‑level data lineage from heterogeneous, non‑standard code and ETL assets, describing the motivation, architectural blueprint, practical challenges such as cost, accuracy and hallucination, and the resulting improvements in impact analysis, metric tracing, and sensitive‑data governance.
This article details Qichacha's technical journey of applying large language models to resolve field‑level data lineage challenges in a complex, multi‑source data environment, describing the motivation, architecture, practical implementation, engineering trade‑offs, and measurable outcomes.
This article explains Paimon's LSM-based storage model, detailing its four-tier architecture (MemTable, Immutable MemTable, L0, L1+), write/read paths, Merge-On-Read with loser-tree K-way merge, four merge engines, compaction triggers and modes, changelog production, and performance tuning practices for lakehouse workloads.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based ad‑hoc analysis to a Lambda‑style architecture and finally to a lakehouse with generic incremental compute, cutting architecture complexity, resource and development costs by one‑third while delivering second‑level queries over trillions of rows.
This article enumerates ten uncommon, compaction‑related problems encountered in large‑scale Paimon deployments, explains their root causes—such as RPC timeouts, snapshot expiration, file corruption, and write conflicts—and provides concrete configuration tweaks and operational steps to resolve each issue.
The article details Xiaohongshu's evolution from a simple ClickHouse‑based analytics layer to a Lambda‑enabled 2.0 stack and finally a Lakehouse‑based 3.0 architecture, showing how each iteration reduced infrastructure complexity, resource consumption and development effort by roughly one‑third while supporting trillions of daily events and AI‑driven use cases.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based ad‑hoc analysis to a Lambda‑style architecture and finally to a lakehouse built on Iceberg, StarRocks, Flink and Spark, cutting architecture complexity, resource and development costs by two‑thirds while supporting trillions of daily events with sub‑second query latency.
The article details Xiaohongshu's four‑stage data‑platform evolution—from a simple ClickHouse ad‑hoc setup to a Lambda‑based 2.0 design and finally a lakehouse‑driven 3.0 architecture—highlighting the adoption of general incremental compute, cost‑reduction to one‑third, performance gains of up to ten‑fold, and the SPOT standards that guide the new system.
Xiaohongshu transformed its data platform from a simple ClickHouse‑based analytics stack to a unified lakehouse with generic incremental compute, cutting architecture complexity, resource cost, and development effort by roughly one‑third while supporting petabyte‑scale, sub‑second queries across its 350 million‑user app.
The article dissects a credit data system architecture, detailing six logical layers—from multi-source data collection and feature engineering (including graph features and feature stores) to model training, real‑time stream processing, decision engine integration, and privacy‑preserving computation—while explaining the trade‑offs, tools, and performance targets needed for accurate, low‑latency risk assessment.
The article presents a Flink‑Fluss‑LLM architecture that captures full‑link agent events via a non‑intrusive hook, combines semantic AI inference with deterministic CEP rules, and delivers millisecond‑level alerts for malicious user detection, tool result poisoning, and chain‑attack risk mitigation.
The article explains Flink Agents' current 0.3 preview, detailing its layered architecture—from Agent definition to execution plan and runtime operators—while outlining the roadmap for Skills integration, Mem0 long‑term memory, durable execution, and observability enhancements aimed at production readiness.
This article compares three practical approaches—direct database storage, a Flink‑Kafka‑Redis‑Grafana pipeline, and an ELK stack—to achieve real‑time API request counting for high‑concurrency scenarios, outlining their architectures, advantages, and trade‑offs.
This article details the design and implementation of a cloud‑native, real‑time monitoring system for Alibaba Cloud OpenAPI, covering background challenges, a Flink‑SLS architecture, multi‑region data processing, checkpoint and state‑backend tuning, source‑side predicate pushdown, visualization with Grafana, and production results.
During a gray‑release of JDK 25 on Ctrip's massive Spark and Flink clusters, silent data corruption appeared in Parquet and ORC files, traced to a G1GC Optional Evacuation bug that moved JNI‑pinned objects, a root cause later back‑ported and fixed in JDK 25.0.3.
In the 2026 spring hiring season, many big‑data job seekers see their resumes disappear because they still focus on offline batch processing, while employers now demand real‑time streaming, AI‑driven data pipelines, and cloud‑native deployment skills such as Flink, vector databases, and Kubernetes.
This article presents a senior architect’s end‑to‑end design of a smart parking guidance system, detailing how high‑frequency sensor streams are ingested via Spring Boot gateways, processed with Kafka and Flink for stateful de‑duplication, debouncing, and aggregation, and finally served through Redis, PostgreSQL and Spring Boot APIs for real‑time vehicle routing.
The article outlines a full‑stack data architecture for 2026, covering edge‑preprocessed collection, unified observability with OpenTelemetry, CDC‑based real‑time sync, a Kafka‑Flink‑Iceberg processing stack, Data Mesh governance, and AI‑Agent powered insight, while offering practical adoption paths and pitfalls to avoid.
This article presents a comprehensive design for a layered storage and unified scheduling platform that archives MySQL historical data, reduces storage costs, ensures high‑performance queries, and enables efficient data analysis through tiered hot, warm, and cold storage using big‑data technologies.
This article details Ant Group’s Flex vectorized engine built on Velox, covering the current state of vectorization, Flex’s architecture (Flink + Velox), core feature development, correctness guarantees, large‑scale deployment results, and future directions for full‑link vectorization and broader hardware support.
This article walks through using Kiro AI IDE to develop an Amazon EMR Flink monitoring system, detailing spec‑driven development, MCP integration, steering rules, the full backend and frontend stack, and shows how the workflow cuts implementation time from 60–80 hours to about 10 hours, delivering a 6–8× efficiency gain.
This article explains how to extend a Flink CDC job that already syncs an entire MySQL database to Doris so that newly created tables are automatically created in Doris in real time, using the CdcTools utility, side‑output streams, and asynchronous I/O.
This article analyzes how Spark and Flink should divide responsibilities in lakehouse architectures by evaluating three core dimensions—data latency, computation accuracy, and resource cost—arguing that mature batch-stream integration balances business needs against technical overhead rather than pursuing universal real-time processing.
This article presents a production‑grade Flink ClickHouse sink that solves common pain points such as lack of size‑based batching, static table schemas, and distributed‑table latency by introducing data‑size batching, dynamic table routing, local‑table writes, load‑balanced node discovery, back‑pressure queues, dual‑trigger flush, and recursive retry with node exclusion, all integrated with Flink checkpoint semantics for at‑least‑once guarantees.
Using a 600k‑record IP range dataset, we built identical tables in ClickHouse and Doris, and a Redis skip‑list store, then ran three Flink‑Kafka streaming jobs to compare query latency across the three databases under varying traffic rates, revealing Redis as fastest, ClickHouse second, Doris slowest.
This article details the evolution of a data warehouse at RenliJia from a MaxCompute‑centric setup to a modern lakehouse using StarRocks, Paimon, Flink, and Fluss, describing design goals, technical evaluations, implementation steps for offline, OLAP, and real‑time workloads, and the challenges and future plans that emerged.
This article details the industry-standard dual pattern of file body storage and metadata registration for unstructured data lake ingestion, covering a three-layer architecture, batch and real-time implementation steps using Paimon and Flink, AI-specific optimizations like label standardization and format conversion, and key operational considerations for permissions, versioning, and cost control.
This article explains how to use Flink CDC together with the CdcTools utility to automatically capture newly created MySQL tables and synchronize both their schema and data to a Doris database in real time, covering the required code, side‑output handling, async execution, and a special delete‑sign field.
The article explains that real‑time data processing requires not only speed but also accuracy, stability, and long‑term maintainability, compares Lambda, Kappa, and unified batch‑stream architectures, discusses lakehouse storage, and provides practical guidance on selecting the most suitable approach for different project needs.
This article provides a practical framework for SMEs to select and implement data platform architectures, comparing traditional warehouses, lakehouse, and multimodal data lakes across cost, ROI, and operational efficiency, with scenario-based recommendations and a lightweight lakehouse implementation guide using open-source stack Flink, Paimon, StarRocks, and MinIO.
This guide walks you through designing and implementing a complete e‑commerce data‑warehouse project—from raw data ingestion and ODS/DWD/DWS/ADS layers to optional real‑time analytics—while highlighting interview‑ready resume tips, common pitfalls, and performance‑tuning tricks.
Backend developers need to understand both offline and real-time data warehouses; this guide explains data collection, layering, partitioning, typical use cases, archiving strategies, and how to build a real-time warehouse with Flink, covering practical steps, examples, and key considerations for efficient data processing.
The article recaps the Apache Hudi Asia Meetup hosted by JD, covering community updates, JD's data‑lake challenges, the upcoming Hudi 1.1 release, JD's architectural redesign, Kuaishou's real‑time lake adoption, and Huawei Cloud's deep optimizations, all aimed at building an AI‑native, real‑time lakehouse.
This article presents a systematic benchmark comparing ClickHouse, Doris, and Redis for IP‑range dimension lookups using Flink‑Kafka pipelines, detailing test design, result table schema, query interfaces, and performance results across varying data rates, concluding that Redis offers the fastest and most stable query latency.
The JD‑hosted Apache Hudi Meetup showcased the 1.1 release’s pluggable table format, Flink performance gains, LSM‑Tree MoR redesign, and AI‑native features such as vector indexing, illustrating how the open‑source lakehouse is evolving to meet BI and multimodal AI workloads.
Ctrip transformed its traditional T+1 offline warehouse into a near‑real‑time lakehouse by integrating Flink CDC with Apache Paimon, designing a two‑stage CDC ingestion, optimizing performance, implementing dynamic updates, and deploying the solution across multiple business scenarios, achieving minute‑level latency, reduced costs, and faster data‑driven decisions.
The 2025 Flink release introduces a disaggregated state management architecture for cloud‑native elasticity, AI‑driven Flink Agents with LLM, Memory and Tool support, Delta Join and VARIANT type for semi‑structured data, adaptive batch execution, incremental checkpoints, high‑speed network optimizations, and new SQL and Process Table Functions, reshaping real‑time analytics.
This article explains the evolution of Vivo's real‑time recommendation feature‑concatenation architecture, compares hour‑level, Redis‑streaming and RocksDB state‑backend solutions, and details the memory, performance, startup and HDFS RPC problems encountered along with the concrete fixes applied.
Apache Kyuubi, an enterprise‑grade multi‑tenant data gateway, replaces Livy and Flink SQL Gateway to support multiple engine versions, cross‑cluster elastic scheduling, high‑availability batch jobs, and traffic control, dramatically reducing deployment complexity, improving resource utilization, and accelerating release cycles for large‑scale Spark and Flink workloads.
The article explains the concepts of data lakes and lakehouses, compares them with traditional data warehouses, outlines the reliability, performance, and security challenges of data lakes, and then details a practical lakehouse implementation using Apache Iceberg, Flink SQL, CDC pipelines, and supporting tools such as Hive Metastore and Trino.
This article explains how small‑file issues in Paimon's streaming data lake architecture degrade system stability and query speed, and presents practical parameter‑tuning, table‑level settings, asynchronous compaction, and monitoring techniques to mitigate those problems.
This article explains the fundamentals of network flow control, compares TCP‑based and credit‑based backpressure in Flink, walks through buffer pool interactions, and shows why dynamic backpressure cannot fully replace static rate limiting in real‑world streaming pipelines.
Leapmotor’s rapid growth to one million production cars drove a shift from daily batch data to minute‑level real‑time analytics, prompting the adoption of Flink as the core engine of a multi‑layered big‑data platform that handles massive IoT signals, supports fault diagnosis, and integrates batch and streaming workloads on the cloud.
Li Auto’s data team tackled the explosion of vehicle‑telemetry data—over a trillion rows and millions of signals per second—by redesigning their data foundation with Alibaba Cloud’s Hologres and Flink, achieving sub‑second latency, elastic scaling, high availability, and significant cost reductions across real‑time and offline workloads.
This guide walks through adding the Elasticsearch sink to a Flink job, configuring connection and bulk settings, implementing custom failure handlers, and enabling checkpoint‑based retry mechanisms to ensure reliable streaming writes.
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.
The article details Zhihu's step‑by‑step evolution from a Spark‑Streaming‑based real‑time data warehouse (v1.0) to a Flink‑Streaming SQL‑driven platform (v2.0), explaining design choices, Lambda architecture implementation, scalability challenges, and future automation plans.
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.
Zepto transformed its brand‑analytics platform from a Postgres MVP into a production‑grade, sub‑second real‑time analytics solution by adopting StarRocks, redesigning its data pipeline with Databricks, Kafka, and Flink, and choosing a storage‑compute architecture that supports massive joins and rapid insights.
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 explains how missing Flink operator UIDs cause savepoint recovery failures when job topology changes, demonstrates the fix with semantic UID naming conventions, and shows why random UUIDs harm maintainability compared to meaningful identifiers like 'kafka-order-source-uid'.
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 details Ctrip Business Travel’s implementation of a near‑real‑time data warehouse using Flink CDC and the Paimon lakehouse engine, covering order wide‑table construction, ticket refund alerts, ad attribution, batch‑stream integration, and practical lessons on Partial Update, Aggregation, and Tag‑based incremental processing.
Lazada transformed its e‑commerce product selection by building a unified, real‑time platform on Alibaba Cloud Flink and Hologres, overcoming data silos, freshness delays, and high‑throughput challenges to enable millisecond‑level decisions across six Southeast Asian markets.
In this detailed case study, Alibaba’s Ele.me team explains how they evolved from siloed, chimney‑style real‑time warehouses to a unified Flink‑Paimon lakehouse, highlighting the three development stages, technology evaluations, the Alake platform’s one‑stop capabilities, production results, and future directions such as Fluss and AI integration.
This article details Ctrip Business Travel’s implementation of a near‑real‑time data warehouse and lakehouse using Flink CDC and Apache Paimon, covering order wide‑table construction, automated ticket reminders, ad attribution, batch‑stream integration, and lessons on Partial Update, Aggregation, and Tag‑based incremental processing.
This article outlines production-tested Flink operator naming conventions emphasizing business-logic clarity, concise action-object-result formats, type-specific patterns for map/filter/join/window/sink, DAG-stage prefixes, and pairing name() with uid() for state recovery, while avoiding technical jargon and dynamic variables.
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.
At VLDB 2025, three Alibaba Cloud papers were accepted: one introduces a disaggregated state‑management architecture for Flink 2.0 that separates storage from compute, another presents a cross‑contrastive learning framework for unsupervised Flink anomaly detection, and the third proposes a multimodal ranking system for identifying root causes of slow queries in cloud databases.
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.
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.
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.
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 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.
This article explains how Alibaba Cloud’s big‑data platform leverages Flink ML to build an intelligent‑operations service that tackles stability, cost and efficiency challenges through time‑series anomaly detection, forecasting and streaming log‑clustering, dramatically reducing latency, complexity and operational overhead.
This article details how a real‑time data warehouse built on Flink, Kafka, HBase and MySQL was redesigned using Paimon to eliminate costly deduplication, handle out‑of‑order events, enable streaming reads, simplify aggregation, replace multiple lookup sources, and achieve faster, more reliable batch repairs, resulting in major resource and operational gains.
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.
This article explains Apache Flink's checkpoint and savepoint mechanisms, detailing their core differences, ideal use cases for automatic failure recovery versus manual structural changes, and key operational considerations for stateful stream processing applications.
An interviewee recounts a challenging first interview that focused on Flink resource configuration, late data handling, and offline data warehouse design, and shares practical advice on attitude, thorough preparation, emphasizing real project storytelling, and post‑interview review to continuously improve performance.
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.
