The article explains how Function Calling enables large language model agents to overcome knowledge staleness and hallucination by invoking external tools—such as search, email, code execution, and databases—to fetch real‑time data, perform actions, and deliver verifiable, multi‑step responses.
The article outlines four AI‑driven breakthroughs reshaping data work—AI‑for‑Data automation, generative‑AI‑enhanced governance, NoETL real‑time lake ingestion, and next‑generation SQL analysis—detailing their problems, concrete case studies, implementation steps, pitfalls, and measurable efficiency gains.
Amid the AI boom in the stock market, many retail investors turn to AI tools, but most are paid plugins or designed for U.S. markets; this article curates ten AI‑driven skills that cover real‑time data, technical analysis, quantitative backtesting, monitoring, and risk alerts specifically for Chinese investors.
The whitepaper explains how the explosive growth, multimodal nature, and real‑time collaboration demands of intelligent connected‑vehicle data create two “impossible triangles,” and how SelectDB’s three technical innovations—Index+Bitmap primary keys, Variant sparse columns, and hybrid full‑text/vector search—enable cost‑effective, high‑performance real‑time analytics across five automotive scenarios with proven case studies from leading OEMs.
Facing mysterious minute‑level data rollbacks caused by Impala's metadata cache, a team migrated from a T+1 Hive‑Impala stack to Apache Doris, achieving real‑time consistency, higher performance, simplified ETL, and reduced operational complexity across their points‑based loyalty system.
This article details Gaode Maps' end‑to‑end lakehouse solution for handling high‑frequency, high‑volume trajectory data, covering the challenges of real‑time visibility, multi‑scenario queries, storage cost, and data silos, and describing the layered storage architecture, performance validation, and future expansion plans.
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.
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.
This article details version 3.0 of a Go‑based stock analysis system that adds sector classification, persistent watchlists, scheduled strategy evaluation, real‑time WebSocket market feeds, K‑line charting with technical indicators, a Vue front‑end, deployment steps, future roadmap, and a public GitHub repository.
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.
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 summarizes how major tech firms such as Vivo, Shopee, Alibaba, and TikTok adopt Apache Paimon to unify batch and streaming data pipelines, improve latency, reduce costs, and optimize storage, highlighting key challenges, architectural solutions, and real‑world performance gains.
This article explains JD's complex recommendation system data pipeline—from indexing, sampling, and feature engineering to explainability and real‑time metrics—highlighting challenges such as data consistency, latency, and the use of Flink for massive, low‑latency processing.
This article presents Huolala’s end‑to‑end implementation of Flink CDC for real‑time data capture, detailing the business background, reasons for selecting Flink CDC over Canal, component comparisons, production‑level platform enhancements, data‑lake integration, validation methods, and future directions for unified data ingestion.
Chronon is an open‑source framework that centralizes feature definitions to guarantee training‑inference consistency, eliminates complex ETL pipelines, and supports real‑time and batch processing across diverse data sources, cutting feature‑development cycles from months to under a week, as demonstrated by Airbnb’s 40,000‑feature deployment.
This article compares HTTP polling, WebSocket, and Server‑Sent Events (SSE) for large‑screen data fetching, explains their advantages and drawbacks, outlines suitable business scenarios, and provides complete front‑end and back‑end code examples for implementing SSE and WebSocket connections.
This article demonstrates how to use ActionOMS to capture incremental changes from OceanBase, stream them to Kafka in various formats, and employ Flink to deduplicate and aggregate transaction data into a daily summary, illustrating a complete real-time data pipeline for financial use cases.
This article details HuoLaLa's logistics platform challenges with petabyte‑scale data, the selection of Apache Flink CDC for stable, compatible, and low‑latency data ingestion, the construction of a multi‑layer CDC capability, migration strategies, measurable performance gains, and future open‑source contributions.
This article presents Ant Group's recent explorations and practices in real-time data warehousing, detailing its architecture, data quality assurance, stream‑batch integration, and future data lake implementation, while highlighting the use of Flink, ODPS, and Paimon for scalable, low‑latency analytics.
This article describes the four-stage evolution of the Lego client‑side logging system—covering its initial zero‑to‑one architecture, the separation of business and technical logs, real‑time reporting improvements, and the latest architecture redesign that boosts performance, reduces overhead, and provides a safe migration path.
This article discusses practical strategies for data and algorithm teams to handle real‑time data inconsistencies, validation difficulties, and communication gaps by emphasizing clear scope definition, realistic technical assessments, proactive risk identification, and the importance of specialized, well‑qualified talent.
Feature engineering, the process of creating input variables for machine learning models, is crucial for banking risk control; this article explains the concepts of features, variables, and metrics, outlines challenges in real‑time feature pipelines, and proposes a practical architecture and best practices for building an efficient, low‑code feature platform.
This article presents Ant Group's recent explorations and practices in real-time data warehousing, covering the system architecture, streaming data quality assurance, flow‑batch integrated applications, and future data lake integration, while sharing technical details and operational insights for large‑scale data processing.
Feishu ShenNuo redesigned its data architecture by integrating Flink, Hudi, and Hologres to create a cloud‑native real‑time data warehouse that supports both millisecond‑level ad monitoring and minute‑level game operations, offering scalable storage, low‑latency queries, and comprehensive monitoring and capacity planning.
This article details CVTE’s adoption of stream computing, describing the company background, the challenges of traditional data pipelines, the design of a CDC‑Kafka integration platform, evaluations of PipelineDB, ksqlDB, Materialize and RisingWave, and the overall impact on real‑time analytics and operational efficiency.
This article describes how NetEase Youdao replaced its Doris‑based real‑time data warehouse with Amoro Mixed Hive, detailing the architectural challenges, the Mixed Hive design, implementation steps, performance optimizations, community contributions, and future roadmap to achieve a unified lakehouse with minute‑level freshness and reduced development and operational costs.
This article presents Ant Group's comprehensive real-time data framework and streaming graph processing engine, detailing its architecture, unified batch‑stream capabilities, and practical applications such as traffic attribution, real‑time OLAP, and user‑behavior intent analysis, while outlining future directions.
This article explains how to build a dual‑stream real‑time data pipeline for big‑data applications, defines construction standards, and details a three‑step high‑fidelity load‑testing process that ensures stability and high availability during peak promotional periods.
This article outlines practical methods for ensuring timeliness, data quality, stability, cost efficiency, agility, and management in real‑time data warehouse pipelines using technologies like Flink and Kafka, while addressing consistency, completeness, and high‑availability concerns.
Didi’s unified real‑time data stack outlines best‑practice component choices for four key scenarios—metric monitoring, BI analysis, online services, and feature/tag systems—detailing pipelines from source to sink, resource‑usage guidelines, and a one‑stop development platform to build stable, high‑performance streaming solutions.
This article details how WeChat's Gemini‑2.0 big‑data platform leverages Apache Pulsar, outlining cloud‑native advantages, load‑balancing refinements, cache and SSD tuning, high‑availability safeguards, and cost‑saving strategies that together enable large‑scale, real‑time, deep‑learning recommendation workloads.
An in‑depth case study of a Chinese bank’s near‑real‑time data platform reveals its evolution from a monolithic CDC pipeline to a split architecture featuring a real‑time data lake and a data‑service bus, detailing component choices, schema‑registry integration, SDK development, observability, and future roadmap.
This article presents a comprehensive case study of Manbang's real‑time data platform, detailing its business background, cloud‑native Flink + Hologres architecture, migration from self‑built clusters, real‑time product features, decision‑making workflows, and future roadmap, highlighting performance and cost benefits.
This article describes the challenges of a highly complex supply‑chain system, the evolution from early MySQL‑based reporting to a modern real‑time data platform using Flink, Kafka, ClickHouse, Hologres and other cloud services, and the tools and lessons learned to achieve low‑latency, high‑throughput analytics.
The article traces Dewu’s supply‑chain data platform from slow MySQL reporting through early CDC‑based wide tables to a Flink‑Kafka‑ClickHouse 1.0 design, then to a more scalable Flink‑Kafka‑Hologres 2.0 architecture that solves upsert and compute‑storage separation, while detailing key operational tricks, code‑generation tools, and future plans for lake‑house integration.
The article describes a modular traffic‑control platform for e‑commerce search and recommendation that lets operators quickly adjust strategies for emergencies, cold‑start items, and experiments, replacing costly multi‑team development with a unified operation center, service center, data hub, algorithmic PID controller, real‑time metrics, independent recall chain, and cross‑scene AB testing, while outlining future extensions.
This article summarizes JD's real‑time data product practice, covering product overview, low‑code real‑time platform construction, stream‑batch integrated architecture, and the three‑layer operational defense model, while highlighting challenges, evolution, user distribution, and future directions.
Facing massive, multi‑source traffic and the need for instant analytics, Kuaigou’s real‑time data warehouse evolved from Spark on‑premise to a cloud‑native stack using Alibaba Blink, Flink, and layered OLAP models, streamlining development, cutting costs, and enabling diverse real‑time applications.
The article details Zhihu's data empowerment team's design and implementation of a low‑cost, high‑response real‑time data platform built on Apache Doris, covering real‑time business metrics, algorithm features, and user profiling, and explains the challenges, architectural choices, tooling, performance gains, and future directions.
Manbang's logistics platform leverages a cloud‑native architecture built on Alibaba Cloud Flink and Hologres to deliver minute‑level real‑time data, feature computation, and decision‑making that dramatically improves SLA, reduces operational costs, and powers intelligent driver‑cargo matching across the ecosystem.
The Xianyu recommendation system, built by backend expert Wan Xiaoyong, evolved from offline scoring to a full‑graph, serverless recall‑ranking pipeline that tackles C2C uncertainties through centralized feature engineering, model compression, staged deployment, flexible experimentation, robust governance, and plans for automated attribution and interpretability.
Learn how to design and implement a real‑time data warehouse for an app’s AB‑test monitoring using EasyData, covering data flow layers, CDC task creation, stream table registration, Flink SQL processing, and BI reporting, with detailed steps, code snippets, and practical tips.
This article analyzes why telecom operators need a real‑time data middle platform, outlines its layered architecture and model design, examines the shift from Lambda to Kappa and lakehouse approaches, and highlights how these innovations enable faster, scenario‑driven insights and competitive advantage.
As markets become increasingly dynamic, companies must build real‑time infrastructure to gain timely insights, and this article explains the three real‑time analytics scenarios, the limitations of traditional stream engines, and how Skylab’s integrated cloud‑native platform and Omega architecture address those challenges.
This article introduces Faust, an open‑source Python library that brings Kafka Streams‑style stream processing to Python, covering its features, installation, a step‑by‑step example, typed data models, and how to run real‑time data pipelines with async support.
Zhihu's data‑empowerment team designed a low‑cost, high‑response real‑time data architecture on Apache Doris that powers business analytics, algorithm features, and user profiling, dramatically improving timeliness, reducing targeting costs, and boosting key performance metrics across multiple services.
This article presents a comprehensive case study of Zhihu's data empowerment team, detailing the design of a real‑time data platform and user profiling system, the challenges faced in scalability, latency, and data quality, and the practical solutions and architectural choices implemented to drive business value.
NetEase Yanxuan’s Olympus platform introduces dynamic page‑floor sorting that automatically reorders product modules in real time using a multi‑armed‑bandit algorithm, delivering faster, more accurate and stable personalized marketing, improving exposure efficiency, ROI and handling peak traffic with sub‑40 ms rendering.
This article examines the challenges of traditional data feature acquisition and presents Ant Group’s ultra‑real‑time client feature center, detailing its architecture, data collection, streaming and script computation, backflow mechanisms, and monitoring to deliver rich, timely, and easy‑to‑use features for AI models.
This article chronicles Netflix’s evolution from a failing batch pipeline to a cloud‑native, multi‑tenant streaming platform across four phases, detailing the motivations, challenges, strategic bets, and patterns that enabled the company to scale real‑time data processing to trillions of events per day.
This article explains Meituan's real‑time data warehouse architecture, covering typical business scenarios, the evolution of its streaming platform, key design challenges, solutions such as unified data models, SQL‑based development, UDF hosting, operator optimizations, and future plans for incremental processing and unified batch‑stream semantics.
This article presents Meituan's real‑time data warehouse platform, describing typical streaming use cases, the evolution of its architecture from Storm and Spark Streaming to Flink, the challenges of development, operations and data quality, and the engineering solutions—including unified SQL, web IDE, UDF hosting, pipeline testing, and operator performance optimizations—implemented to support large‑scale, low‑latency analytics.
This article explains the concept, challenges, and key business goals of a real‑time Customer Data Platform, details the technology stack selection—including Nebula Graph, Apache Flink, Apache Beam, Kudu, and Doris—and describes the modular architecture, data model, identity service, streaming computation, storage layers, rule engine, operational results, and future directions.
The talk by Tang Chuxi of Meituan explains typical real‑time data scenarios, the challenges faced when building a streaming data warehouse, and the design, development, operation, and performance‑optimisation solutions implemented on a Flink‑based platform to support massive, low‑latency business applications.
This article details a lakehouse architecture built on Flink and Iceberg that addresses Hive‑based warehouse limitations by enabling ACID transactions, incremental snapshots, stream‑batch unification, CDC support, and various operational optimizations, ultimately achieving near real‑time data ingestion and analytics.
This article explains how a user‑behavior data dashboard originally built on a daily T+1 batch process was redesigned to achieve real‑time T+0 updates by introducing message‑queue notifications, a dedicated aggregation service, and MongoDB storage, improving data freshness and user experience.
This article details the design and implementation of a real‑time, TB‑scale bill‑detail query platform that leverages Kafka for streaming, Debezium and Confluent Platform for change capture, Kudu for low‑latency storage, and Presto/Kylin for fast OLAP queries, while outlining deployment, integration, and future enhancements.
This article details the requirements, architecture, and implementation of a real-time and offline integrated query system, covering data ingestion via Debezium and Confluent Platform, storage in Kudu and HDFS, query engines Presto and Kylin, and strategies for data synchronization, partitioning, and scaling.
The Shenzhen Elasticsearch technical event, co‑hosted by the Elastic Chinese community and Tencent Cloud, presented practical sessions on optimizing the Elastic Stack for search, real‑time analytics, logging, security and APM, featuring compression encoding, MongoDB fusion, ByteDance extensions, cost‑effective log storage, Lucene indexing, cross‑cluster replication, vector engine integration, and large‑scale case studies from Tencent, Tiptop Data and vivo.
This article details Ctrip Finance's end‑to‑end data‑foundation architecture that uses MySQL binlog collection via Canal, Kafka streaming, Spark‑Streaming persistence to HDFS, and a merge process to produce timely MySQL‑Hive snapshots, addressing performance, consistency, and delete‑handling challenges.
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.
This article describes Tencent Games' end‑to‑end real‑time data platform, covering its construction background, the unified OneData development framework, the OneFun data‑service API layer, micro‑service and ServiceMesh management, and the operational benefits achieved through automation, standardization, and scalability.
The article describes Uber’s Gairos platform—a unified real‑time data ingestion, storage, and query system built on Kafka, Elasticsearch and RT‑Gairos—detailing its architecture, Uber use cases such as dynamic pricing, the scalability and reliability challenges faced, and the suite of optimization techniques (sharding, query routing, caching, index merging, template tuning, and data‑pruning) applied to achieve low latency and high throughput.
This article details how Meituan built a minute‑level, high‑throughput real‑time feature platform for its delivery business, covering the business model, six‑layer architecture, data processing challenges, stability measures, scaling achievements, and future roadmap to support millions of orders per minute with sub‑50 ms latency.
The paper presents a configurable real‑time rule engine for live‑streaming product audits that decouples data aggregation from rule execution, uses QLExpress for dynamic conditions, supports Dubbo and HTTP sources, and enables safe gray‑release updates, cutting the rule‑change cycle from weeks to near‑real‑time.
This article explains how crawler engineers can fetch real‑time data such as sports scores, stock quotes, or cryptocurrency prices by comparing polling and WebSocket approaches, introducing the aiowebsocket library, and providing complete Python code to perform handshake, subscription, and continuous data streaming.
This article explores the evolution from traditional to modern data warehouses, outlines the key capabilities of real‑time data platforms such as data real‑time, virtualization, democratization and collaboration, and presents a comprehensive architecture design with unified collection, streaming, compute and visualization layers, while discussing functional, quality, stability, cost, agility and management considerations.
This article explains how the Alibaba DChain Data Converger (ADC) automatically creates wide‑table SQL for real‑time cross‑database analytics by using a pipeline architecture, priority‑queue‑driven task scheduling, and specific design patterns to handle metadata, joins, and resource management.
This article introduces four change‑data‑capture solutions—Canal, Maxwell, Databus, and Alibaba Cloud DTS—explaining their principles, processing steps, key features, and advantages for real‑time data replication and synchronization across diverse database systems in modern data engineering environments.
This article details the 2015 hybrid data‑warehouse design implemented at Guangdong Huaxing Bank, explaining its real‑time, historical, and archival layers, the data‑bus concept, and how mixing in‑memory, relational, and Hadoop technologies addressed modern banking data‑volume, latency, and unstructured‑data challenges.
This article examines the design of a real‑time data platform, discussing its background concepts, modern data‑warehouse perspective, architectural layers, unified data‑collection, streaming, compute and visualization platforms, and the functional, quality, stability, cost and agility considerations required for building an end‑to‑end real‑time pipeline.
This article outlines a comprehensive real‑time data system built on Apache Flink, covering its application scenarios, layered architecture, data model stratification, construction methods, and a concrete Flink SQL example for calculating UV metrics from Kafka‑sourced page‑view data.
The 12306 railway ticket platform faces extreme operational pressure because it must synchronize real‑time sales across online and offline channels, handle countless route and time combinations without buffering, manage complex inventory updates, enforce purchase limits, and process massive concurrent queries, making its load far greater than typical e‑commerce systems.
The article reviews the September 21 cloud‑development salon, detailing how Mini‑Program cloud services simplify backend setup, introduce serverless cloud calls, real‑time database push, value‑added functions and network interconnect, and showcases practical use cases such as game data streaming, AI chatbots, SCRM construction and movie‑ticketing operations.
This article explains how Mogu’s advertising system designs and evolves a real‑time data pipeline—covering merchant and operation needs, data collection, cleaning, processing with Storm, Flink, and Kylin, and service guarantees—to enable high‑quality, low‑latency analytics for advertisers and the platform.
This article explains Apache Kafka as a distributed streaming platform, detailing its key features, core APIs, topic and log architecture, partitioning, consumer groups, guarantees, and how it serves both messaging and storage roles for real‑time and batch processing in big‑data environments.
The article presents an in‑depth overview of the DATABUS data integration platform, detailing its background, current challenges, core capabilities such as data syncing, metadata automation, real‑time subscriptions, and its reliance on TiDB, TiSpark, Hudi, and related big‑data technologies to enable near‑real‑time data warehousing.
This article outlines the background of Ele.me's recommendation business, details the evolution of its recommendation algorithms from rule‑based models to deep learning and online learning, and explains the practical implementation of real‑time data pipelines, feature engineering, model training, and deployment.
This article examines the high‑real‑time requirements of incremental data ingestion for search engines, compares three update schemes, and details how adopting a Kafka subscription‑based message‑queue approach dramatically improves latency and flexibility for the Nuomi search framework.
Meituan designed a Storm‑based real‑time data processing platform that guarantees at‑least‑once delivery and high availability, employs a custom spout, regression‑driven traffic smoothing, and a low‑latency KV store with atomic operations, persisting results in Kafka, MySQL and Cellar to power merchant dashboards and heat‑tag analytics, while planning broader real‑time analytics expansion.
In a keynote to Suning, Intel’s Jon Stine outlines the four retail‑driving forces and ten emerging technologies—ranging from AI and computer vision to 5G and IoT—that will transform stores into intelligent, data‑driven experiences for the next decade.
This article introduces Flink’s background, architecture, and ecosystem, then demonstrates a step‑by‑step tutorial on using Flink SQL to clean and transform streaming data from Kafka, highlighting its advantages over Storm for real‑time ETL.
This note distills Jay Kreps' extensive blog on logs, explaining their core role in distributed databases, real‑time data pipelines, replication, and state‑machine consistency, and showing how logs unify concepts from version control to streaming architectures.
The article describes how Qunar designed and evolved its Prism real‑time data platform—leveraging ELK, Kafka, Spark, Docker, and Mesos—to improve data collection, monitoring, and analysis, reduce deployment time, and support scalable DevOps operations across the company.
This article describes how Qunar’s Prism platform combines ELK, Kafka, Spark, Docker and other open‑source tools to create a real‑time data pipeline that speeds up problem localization, reduces deployment time, and improves resource utilization across development and operations teams.
Exploring how a securities firm processes billions of daily stock‑market indicators in real time, this article compares an in‑process Redis + Lua solution with an out‑of‑process Goroutine‑based architecture, detailing data flow, performance trade‑offs, and scalability considerations for high‑frequency time‑series workloads.
This article details Ctrip's journey building a unified real-time data platform—covering business motivations, architectural requirements, technology choices like Kafka and Storm, implementation of Avro schemas, monitoring, alerting, operational lessons, and future explorations such as Streaming CQL and JStorm.
This article chronicles the development, design decisions, and technical achievements of Alibaba's Data Replication Center (DRC), a real‑time heterogeneous database synchronization platform that has become a core infrastructure for multi‑active data centers, large‑scale e‑commerce, and cloud services.
This article explains the design of a distributed, real‑time price‑update service that handles massive product data, combines query‑driven crawling, observer‑pattern notifications, and multiple data sources to keep e‑commerce price and inventory information fresh within minutes.
