This article compares three standard internet data‑query APIs—OData, GraphQL, and ORDS—examining their REST foundations, query capabilities, metadata handling, versioning, and practical trade‑offs to help developers choose the most suitable API for analytics, integration, and data‑management applications.
This article introduces Apache SeaTunnel, a distributed, high‑performance data integration platform built on Spark and Flink, outlines its technical features, workflow, and plugin ecosystem, and details a concrete traffic‑management use case involving incremental Oracle‑to‑warehouse data synchronization with Spark resources and scheduled shell scripts.
This article introduces the concept and practice of Change Data Capture (CDC), explaining why CDC is needed for real‑time analytics, how it works by capturing DML changes, modern approaches using transaction logs, and key considerations for building a production‑ready CDC system.
This guide walks through Alibaba Cloud EventBridge’s event standardization, shows step‑by‑step how to integrate OSS events for automatic file unzipping, demonstrates custom event source filtering to store data in RDS, and explains using EventBridge event streams to route RocketMQ messages to MNS, complete with code snippets and configuration details.
This article presents a step‑by‑step tutorial on using the OpenMLDB Pulsar Connector to stream real‑time data from Apache Pulsar into OpenMLDB, covering connector architecture, key features, Docker‑based installation, sink configuration, schema registration, message production, verification queries, and future roadmap details.
The article introduces Flink CDC 2.2, highlighting its expanded support for twelve data sources—including OceanBase, PolarDB‑X, SqlServer, and TiDB—while detailing core features such as the incremental snapshot framework, multi‑version Flink compatibility, dynamic table addition, and numerous bug fixes and performance improvements.
Enterprise architects align IT strategy with business goals by managing application portfolios, technology risk, IT operations, security, data integration, and financial considerations, while balancing long‑term strategic planning with tactical execution in a rapidly changing environment.
NetEase Yanxuan replaced its cumbersome data‑warehouse with a flexible Delta‑Lake/Iceberg data lake, creating a unified metadata layer and real‑time ingestion pipelines that cut latency from nightly batches to seconds, slashed compute and storage costs, supported diverse business scenarios and machine‑learning feature engineering, and set the stage for broader future expansion.
This article provides a comprehensive, hands‑on tutorial for connecting a Hive data warehouse to ClickHouse via Seatunnel, covering environment setup, Hive and ClickHouse table creation, full and incremental data import scripts, execution examples, and practical troubleshooting tips.
SPL is an open‑source structured data processing language that extends full SQL‑92 functionality to a wide range of data sources—including CSV, Excel, JSON, NoSQL and Hadoop—allowing developers to perform complex queries, multi‑step calculations, and mixed‑source analytics without a traditional relational database.
The article explains how Kafka serves as the core of a real‑time data warehouse for search, detailing its advantages over traditional databases, integration with Flink for low‑latency stream processing, architectural patterns such as Lambda/Kappa, scaling challenges, and comprehensive monitoring using Kafka Eagle.
The article details Vipshop's multi‑dimensional use of SeaTunnel to integrate Hive and ClickHouse, describing data import/export challenges, tool selection among DataX, SeaTunnel and Spark, custom configurations, platform integration, and future improvements for high‑performance OLAP pipelines.
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.
This article examines the design and implementation of a tenant‑level real‑time Customer Data Platform, detailing CDP fundamentals, business and technical challenges, key architectural components, technology selections such as graph databases, stream processing, storage engines, and the operational practices that enable high‑throughput, low‑latency data integration and analytics.
This document outlines the logical and technical architecture of a government shared resource platform, describing application system upgrades, data collection and analysis, multi‑layer system design, standards compliance, interface management, and overall system integration for improved service quality and decision support.
This presentation details Alibaba Cloud DataWorks Data Integration (DataX), covering its architecture, core design principles, offline and real‑time synchronization mechanisms, deployment modes, product positioning, use‑case scenarios, and its role within the broader DataWorks ecosystem, highlighting its capabilities for large‑scale data movement and processing.
The article reflects on a CTO’s evolution through three stages, examines why architecture, data, servers and R&D management cannot be fully unified in large organizations, and offers practical guidance on innovation, team growth, and establishing a coherent R&D culture.
This article presents a comprehensive overview of medical knowledge graph development, covering global and domestic progress, domain characteristics, a detailed seven‑piece ontology and "Huizhi" graph construction process, platform support, and real‑world healthcare applications such as intelligent alerts, guideline recommendations, and data reporting.
SeaTunnel, the China‑originated data‑integration platform built on Spark and Flink, has been accepted into the Apache Incubator, and this article introduces its history, architecture, plugin ecosystem, deployment requirements, and numerous enterprise deployments across batch and streaming big‑data scenarios.
This article presents a comprehensive overview of medical knowledge graph development, covering global and domestic progress, domain characteristics, a six‑step construction workflow—including schema design, ontology term set creation, and graph building—and showcases practical applications such as intelligent alerts, guideline recommendations, and data direct reporting.
The article explains enterprise integration as the essential practice of connecting applications, data, and devices across distributed, cloud‑native environments, covering its evolution, key challenges, and core techniques such as messaging, application connectors, data flow platforms, integration patterns, and APIs.
This article outlines Ctrip Vacation's data standard management approach, covering why standards are needed, the three‑element framework of scope, tools, and policies, and detailed practices for data integration, production change handling, metadata governance, portal dashboard standardization, and self‑service query templating.
This article explains how to integrate Apache Hudi with Flink CDC to build a near‑real‑time data lake, covering Hudi’s storage model, streaming primitives, version compatibility, Maven setup, SQL table definitions, data flow from MySQL through Kafka, and practical troubleshooting tips.
This article introduces the cloud‑native LakeHouse solution from China Mobile Cloud, covering its lake‑warehouse integration concept, overall architecture, core functions such as storage‑compute separation, one‑click data ingestion, intelligent metadata discovery, serverless execution, JDBC support, incremental updates, and typical application scenarios in public and private clouds.
The article summarizes a GOPS conference presentation by Dingmao Technology on AIOps scenario‑driven construction, detailing challenges, definition of scenarios, technical methods, roadmap planning, and future prospects, while showcasing practical examples and supporting technologies for intelligent IT operations.
This article explains why ID mapping is essential for breaking data silos, describes traditional address cleaning and DSP scenarios, and details a graph‑based, six‑step process that builds a unified One‑ID dictionary to enable comprehensive user profiling and analytics in big‑data environments.
This article introduces Flink CDC 2.0, explains its distributed full‑load and incremental reading mechanisms, details the slice partitioning, snapshot correction, and binlog handling logic, and provides a complete Java example that demonstrates how to configure Flink SQL, MySQL source, and Kafka sink.
This article explains the background, challenges, overall architecture, core technology optimizations, edge‑computing integration, data modeling, serialization, and real‑world case studies of moving industrial IoT data to Alibaba Cloud, illustrating how cloud‑native solutions enable digital transformation in manufacturing.
The article outlines a typical big data platform architecture, detailing its core layers—data collection, storage and analysis, sharing, application, real-time computation, and task scheduling—while describing key technologies such as Flume, DataX, HDFS, Hive, Spark, Spark Streaming, and Redis.
This article explains the fundamentals of ETL, describes data‑warehouse architectures such as star and snowflake schemas, outlines a five‑step enterprise‑level ETL workflow, and discusses advanced techniques, tools, and algorithms for building robust data‑warehouse solutions.
This article provides an in‑depth technical overview of Flink CDC 2.0, covering its CDC fundamentals, comparison of query‑based and log‑based approaches, the new lock‑free chunk algorithm, FLIP‑27 based parallel snapshot reading, performance benchmarks, documentation improvements, and future roadmap for stability and ecosystem integration.
The article explains what a Customer Data Platform (CDP) is, why businesses need it to overcome fragmented multi‑channel data, enable fine‑grained operations and data‑driven growth, and outlines the key steps for building a CDP, including data collection, OneID unification, tagging, lifecycle management, and marketing execution.
This article explains the growing analytical demands in the big‑data era, the limitations of traditional OLAP, and how Kyligence’s distributed OLAP engine addresses data‑island issues, multi‑dimensional and many‑to‑many analysis, unified security, and performance optimization with MDX on Spark, delivering a seamless Excel‑like experience.
This article walks through the complete process of creating a high‑impact visual dashboard for large‑screen displays using FineReport, covering tool selection, data preparation, report creation, design, visual polishing, and adding dynamic effects.
This article provides a comprehensive guide to Spring Batch, covering its purpose, architecture, core components such as Job, Step, ItemReader/Writer/Processor, chunk processing, skip strategies, configuration tips, and common memory issues, all illustrated with code examples and diagrams.
This article explains what a big data platform is, outlines its seven‑component overall architecture, details the technical stack from data sources to applications, and describes the key subsystems such as catalog management, data integration, governance, storage, processing, sharing, development, and analysis.
The article details how NetEase Cloud Music co‑built a unified data service platform with NetEase YouShu, describing its architecture, phased development from internal use to online high‑concurrency services, feature enhancements such as API marketplace, multi‑source support, parameter conversion, and future roadmap for broader data products.
This article presents NetEase Game's Flink‑based streaming ETL system, detailing business background, log classifications, specialized and generic ETL services, Python UDF integration, runtime optimizations, HDFS write tuning, SLA metrics, fault‑tolerance mechanisms, and future roadmap for unified data lakes and PyFlink support.
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.
The article explains how Apache Kylin and MLSQL complement each other, detailing Kylin's OLAP strengths, MLSQL's data‑processing and AI capabilities, and demonstrates a low‑code integration that enables users to perform ETL directly within Kylin’s interface while outlining future deep‑link scenarios.
This article explains how Flink SQL can be used to construct a unified stream‑batch ETL pipeline for data warehouses and data lakes, covering data integration, CDC support, streaming writes to Hive and Iceberg, and various join techniques such as regular, interval, and temporal joins.
This article introduces Kafka Connect, explaining its purpose as a scalable and reliable tool for moving data between Apache Kafka and external systems, detailing its core concepts, architecture, deployment modes, configuration files, and a step‑by‑step example that streams data from a file source to a file sink.
The article reviews Flink 1.12's major enhancements, including comprehensive SQL capabilities, deep integration with Hive via catalog and streaming support, and a practical code example that demonstrates how to write streaming data into Hive tables while handling partition commits and small‑file merging.
The article explains how traditional point‑to‑point data integration leads to data quality, consistency and cost issues, introduces the concept of data services as a unified, reusable way to provide data, outlines their benefits, lifecycle stages, classification into data‑set and API services, and presents Huawei’s practical construction strategy and the “Three‑1s” supply‑chain goals.
This article introduces Ballerina’s unique language features, including native XML/JSON data types, datatable handling, inline definitions, parallel processing with workers and fork‑join, and the comprehensive development toolset such as Composer, Testerina, connectors, and editor plugins, illustrating code examples throughout.
This article explains how Spark can be combined with MongoDB for large‑scale data processing, covering Spark fundamentals, comparisons with HDFS, practical integration patterns, performance benefits, real‑world case studies, and detailed code examples for deployment and analytics.
This article provides an in-depth overview of ETL concepts, including extraction, transformation, loading, data warehouse architecture, and detailed discussion of Kettle (Pentaho Data Integration) features, design principles, components, transformations, jobs, database connections, metadata management, and practical examples for building robust data integration pipelines.
This article introduces Alink, Alibaba's open‑source machine‑learning platform built on Flink, explains its core algorithms, performance comparison with Spark ML, version‑wise feature evolution, and provides practical quick‑start instructions for both Java (Maven) and Python (PyAlink) users, including data source handling, type conversion components, unified file‑system operations, and an overview of its FM algorithm implementation.
Flink SQL, the high‑level SQL interface for Apache Flink, offers language‑independent, dependency‑free, easy‑to‑use stream processing with advanced features such as DDL, UDFs, time semantics, windowing, pattern matching, and built‑in connectors, supporting data synchronization, batch‑stream fusion, Hive integration, and various product enhancements.
This article provides a comprehensive guide on integrating Hive with Spark SQL, covering Hive‑on‑Spark and Spark‑on‑Hive setups, spark‑shell and spark‑sql usage, HiveServer2 with Beeline, Scala scripts for reading and writing Hive tables, and partition handling for aggregated results.
The article explains the fundamentals of ETL, compares Talend and Pentaho in terms of openness, connectivity, support, performance, GUI usability, deployment flexibility, and cost, and concludes with guidance on choosing the appropriate tool based on specific business and technical requirements.
The article outlines Qunar’s challenges in aggregating international hotel data, analyzes issues such as localized address formats and limited text similarity parsing, and presents a pattern‑matching and weighted scoring approach that improves aggregation accuracy across multiple countries.
The article details Meituan Delivery’s A/B testing platform and evaluation system, explaining its closed‑loop design, multi‑strategy traffic allocation with AA grouping, comprehensive metric hierarchy, statistical rigor, data integration, and implementation architecture, and outlines future tools for traffic‑volume recommendation.
This article presents a comprehensive technical overview of the Real-time Data Platform (RTDP), detailing its overall architecture, component selection—including DBus, Kafka, Wormhole, Moonbox, and Davinci—design philosophies, functional features, and various deployment patterns such as synchronous, stream-processing, rotation, and intelligent modes.
This article explains the differences between Spark on Hive and Hive on Spark, then provides step‑by‑step instructions for configuring Hive MetaStore, setting up SparkSQL to use Hive, and demonstrates a complete Scala program that creates a Hive table, loads data, and queries it.
The article examines China's push to develop data factor markets and showcases how Suning’s integration of online and offline data, big‑data analytics, and AI is revitalizing traditional retail, illustrating the broader impact of digital transformation on the post‑pandemic economy.
Amap transformed its fragmented POI deep‑information pipelines into a unified platform that automates data acquisition, parsing, dimension alignment, specification mapping, and lifecycle management across billions of records, enabling product managers to integrate, debug, and scale diverse content‑provider feeds with real‑time, end‑to‑end control.
This article demonstrates how to build a reusable Python class with pyodbc to connect to Microsoft SQL Server, import CSV files, create tables, and perform common operations such as push, union, and drop, providing complete code examples and explanations for each step.
The article explains how Beike Real Estate constructs an industry knowledge graph by integrating internal and external data, outlines the technical framework and data processing steps, and demonstrates its AI-driven applications such as intelligent Q&A, recommendation, and decision support for the real‑estate market.
The article explains why modern companies rely on data‑driven decisions, outlines the two main challenges of tracking data and connecting it to BI, describes the three‑step analytics stack (integration, warehouse, analysis), and highlights the cost, flexibility, and security advantages of open‑source tools.
The article explains the concept, architecture, and key components of a data middle platform—including data aggregation, development, asset management, service systems, and operational and security mechanisms—while also promoting related books and a giveaway.
To generate a user‑enriched order report in a microservice system, the article compares four approaches—direct DB access, REST data aggregation, batch pulling, and an event‑driven model—highlighting their trade‑offs in coupling, performance, scalability, and resilience, and recommends the event‑push solution.
The article describes the background, architecture, and design principles of a unified big‑data scheduling and data‑exchange platform, detailing its data ingestion “direct‑train”, centralized scheduling engine, and DataX‑based data‑exchange components along with monitoring, alerting, and security features.
Beeswax Finds Home developed a middle platform to integrate data and business processes in the real estate industry, enhancing efficiency and enabling new business models through shared capabilities and standardized solutions.
This article presents a structured, repeatable approach for Talend data‑integration jobs that guides readers through pinpointing performance bottlenecks, testing individual pipeline stages, and applying targeted optimizations to sources, targets, and transformations to achieve higher throughput and more reliable ETL processes.
This article details the end‑to‑end design and implementation of a custom tool—named “Crystal Palace”—that automates financial reporting across App Store, Google Play, Facebook and Amazon, turning a tedious manual reconciliation process into a scalable, data‑driven solution for game publishers.
This guide introduces the canal‑kafka integration package, outlines its constraints, and provides a step‑by‑step tutorial with Maven dependencies and Java source code for a SimpleCanalClient, a Kafka producer, and a server class, enabling a functional demo of Canal to Kafka data streaming.
The article introduces Snowball's AIBO data middle platform, detailing its storage‑compute separation architecture, core capabilities such as data integration, catalog, tagging, analysis tools, micro‑service data APIs, and outlines future enhancements for security, lineage, and continuous business‑driven iteration.
This article outlines how 360’s data center built QuickSQL, a federated SQL engine that unifies queries across heterogeneous sources such as Hive, MySQL, and Elasticsearch, detailing the business challenges, architectural design, performance benchmarks, and future roadmap for multi‑source data analysis.
This article introduces TiSpark—an extension of Spark that tightly integrates with TiDB/TiKV to enable high‑performance, scalable data synchronization and OLAP queries, details its architecture, key configuration, performance advantages over Spark SQL and Sqoop, and outlines its role in the Databus data‑integration platform.
Quicksql is an open‑source, cross‑data‑source SQL engine built on Apache Calcite that provides a unified, safe, and fast SQL interface, enabling users to query heterogeneous storage systems such as Hive, MySQL, Elasticsearch, and Druid through command‑line, API, or JDBC connections.
The article presents 360’s challenges with heterogeneous, high‑volume data sources, explains the design of a unified federated SQL engine called QuickSQL that leverages Apache Calcite, Spark, Flink and other back‑ends, and evaluates its performance and future development directions.
The article examines how fragmented SaaS marketing stacks limit real‑time data use, explains the evolution from early CRM to marketing automation, highlights the shortcomings of MQL models, and shows how Customer Data Platforms (CDPs) restore data continuity, scalability, and campaign effectiveness.
This article explains how Apache Pulsar and Apache Flink can be combined to provide a unified, scalable, and fault‑tolerant data processing platform, covering Pulsar's architecture, its differences from other messaging systems, various integration patterns, and concrete code examples for stream and batch workloads.
This guide walks you through installing JDK, Logstash, Ruby, and required plugins, configuring Logstash to pull data from a MySQL table, and sending it to Elasticsearch, including code snippets, configuration files, and troubleshooting tips for a smooth data synchronization.
This article outlines the evolution of knowledge graphs, describes typical enterprise application scenarios, compares graph database options such as Neo4j, Cayley and Dgraph, and presents a six‑step methodology for building, storing, and applying knowledge graphs in large‑scale business environments.
To handle terabytes of daily data and diverse business needs, the company built a three‑layer basic data platform—collection/computation/storage, unified data management, and API‑driven services—augmented by a standardized collection system, a robust Domino scheduler, and a self‑service analysis tool, aiming to evolve into a full data‑middle‑office for end‑to‑end intelligence.
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 introduces the fundamentals of Business Intelligence, explains what ETL and data warehouses are, compares them with traditional databases, and outlines the main characteristics and popular tools such as Hive used in modern big‑data environments.
Quicksql is an open‑source unified SQL query engine that simplifies and secures cross‑data‑source queries by providing a consistent ANSI‑based language, automatic engine selection, and support for mixed queries across Hive, MySQL, Elasticsearch, and other platforms, reducing learning and integration costs.
This article explains how Baidu Cloud's Noah intelligent operations product builds a unified operations knowledge base by categorizing metadata, status, and event data and applying three ETL approaches—Pull, Push, and Lazy—to handle offline, near‑line, and real‑time data integration.
The Youzan Search Platform evolved from a simple Elasticsearch cluster in 2015 to a modular, message‑driven architecture with proxy validation, caching, and management tools, and now plans a cloud‑native, Kubernetes‑based 4.0 version that automates data sync, isolates workloads, and scales elastically to support billions of records.
Aries, an internally built MySQL incremental log distribution platform, leverages GTID‑based binlog dumping to achieve stable, consistent, and real‑time data synchronization across heterogeneous systems, supporting use cases such as Elasticsearch sync, cache updates, archiving, and live statistics.
This article explains the architecture of a Real‑Time Data Platform (RTDP), details the technical selection of core components such as DBus, Kafka, Wormhole, Moonbox and Davinci, and discusses data management, security, operations, and four deployment modes—synchronization, flow, rotation and intelligent—illustrating how each fits different business scenarios.
The article describes the design, data integration, ID mapping, tag generation, and application scenarios of the 58 user profiling platform, which aggregates billions of user IDs across multiple business lines to provide online and offline persona data for personalization, analytics, and AI modeling.
The article outlines seven common big‑data initiatives—data integration, specialized analytics, Hadoop‑as‑a‑service, stream processing, complex event handling, ETL pipelines, and SAS replacement—explaining their goals, typical technologies such as HDFS, Hive, Spark, Storm, Kafka, and practical considerations for enterprises adopting Hadoop ecosystems.
This article details Qunar's journey of automating application operations, covering the evolution of their host‑management system, unified monitoring/alert platform, and data‑interchange mechanisms that enabled the company to grow from a few hundred to over ten thousand servers with a stable six‑person ops team.
The article introduces Transwarp Transporter, a near‑real‑time ETL tool for TDH 5.x, explains its architecture, visual dashboard, drag‑and‑drop data‑flow design, debugging features, parameter management, and highlights how it empowers business users to achieve fast, reliable data migration in big‑data environments.
This article provides an extensive overview of more than ninety open‑source and commercial big‑data tools—including ETL platforms, resource managers, storage systems, messaging queues, processing engines, and visualization libraries—detailing their core functions, typical use cases, and notable adopters.
The article explores the evolution of big‑data applications in China, from Oracle’s trend report and the concept of "omni‑domain data" to UMENG+’s technical architecture, unified tech stack, AI integration, and future directions for delivering real customer value.
The article explains how Apache Kafka has become a pivotal, high‑scalable publish‑subscribe system in the big‑data ecosystem, addressing the limitations of traditional databases, enabling real‑time data integration across specialized distributed systems, and shaping future data‑governance practices.
This article explains how logs—simple, append‑only, time‑ordered records—serve as the core abstraction behind databases, distributed systems, data integration pipelines, and modern stream‑processing platforms such as Kafka and Hadoop, illustrating their design, scalability, and practical challenges.
This article explains how logs—simple, append‑only, time‑ordered records—serve as the fundamental abstraction behind databases, distributed systems, data integration pipelines, and stream‑processing platforms like Kafka and Hadoop, illustrating their role in ordering, replication, scalability, and real‑time analytics.
