This article breaks down the concept of a Data Agent that automates the entire traditional data‑analysis pipeline, explains its three‑layer architecture, the ReAct reasoning loop, multi‑agent collaboration, six practical use cases, and offers deployment recommendations for teams looking to adopt AI‑driven data workflows.
The article explains how Kuaishou partnered with Apache Hudi to overhaul its ODS‑based data lake, addressing latency, storage cost, and complexity for AI and BI workloads, detailing the evolution from mysql‑to‑hive to mysql‑to‑hudi 1.0 and 2.0, the resulting performance gains, cost savings, and future roadmap.
The article explains how Kuaishou modernized its data lake by partnering with Apache Hudi to address latency, storage cost, and consistency issues in both AI and BI pipelines, detailing architectural changes, new ingestion tools, partitioning strategies, compaction mechanisms, performance gains and future plans.
The article explains how Dimensional Query Language (DQL) replaces traditional multi‑table JOIN syntax with object‑dot notation and dimension‑alignment concepts, offering simpler, more business‑focused queries for BI scenarios, and demonstrates its implementation through concrete SQL examples and a real‑world engine integration.
This presentation by iQIYI’s Technical Director Zhang Xiaoming details the evolution of BI systems, introduces the Chat BI framework, explains its three‑step implementation, outlines architectural design, data‑warehouse integration, performance optimizations, and user‑operation strategies, revealing how AI and RAG empower smarter data analytics.
Tired of costly analytics dashboards, this guide showcases nine open‑source Python libraries—including Plotly Dash, Superset API client, Ibis, Lux, Redash‑API‑Py, Kibana‑API, Panel, Evidently, and Metabase‑Py—that let you create, automate, and monitor interactive visualizations and data pipelines for free.
This article outlines Kuaishou Data Platform's mission to boost data decision efficiency, describes its three‑layer architecture, explains the BI process from data ingestion to application, and shares practical experiences and future outlook for intelligent BI powered by AI and big data.
This article outlines Kuaishou's Data Platform team's mission to boost data‑driven decision making through advanced compute engines, high‑performance services, and AI‑enhanced BI, detailing its architecture, challenges, solutions, and future outlook for large‑scale intelligent analytics.
This article outlines how Kuaishou’s Data Platform Department enhances decision‑making efficiency by building advanced compute engines and high‑performance services, detailing the platform’s architecture, challenges of intelligent BI, AI‑driven solutions, and the end‑to‑end BI workflow from data ingestion to analysis.
This article explores how the convergence of business intelligence and large‑model generative AI creates new business value, outlines the evolution of BI technology through three stages, and details Baidu’s ChatBI design, implementation challenges, and real‑world impact.
This article presents a curated list of advanced topics covering AI-driven data governance, large‑model applications, intelligent operations, and BI+AI practices from industry leaders such as JD, Alibaba Cloud, Ping An, Didi, and Kuaishou, along with a QR code to download the full e‑book.
In the era of big data, the article surveys the most popular open‑source and commercial BI platforms—highlighting their core features, integration capabilities, community activity, star counts, and limitations—to help readers choose the right visualization tool for self‑service analytics.
This article details how the vivo Internet Big Data team systematically improved the home‑page performance of their BI data‑visualization platform by analyzing key web metrics, identifying bottlenecks, and applying time‑ and space‑focused optimizations such as HTTP/2 migration, resource preloading adjustments, code splitting, and Web Workers, resulting in a 292% overall speed increase.
This article explains fact tables in data warehousing, covering their definition, granularity, additive classifications, null handling, consistency rules, and the various types such as transaction, snapshot, cumulative, fact‑less, and aggregate tables, along with design trade‑offs and ETL considerations.
Xiaohongshu tackled low reuse of APP tables, limited scalability of single-table BI datasets, and poor dashboard query performance by introducing logical datasets and materialized views, which enable query pruning, reduce data redundancy, and accelerate BI queries, achieving up to 80% latency reduction and higher hit rates.
This article introduces ChatBI, NetEase’s AI‑driven BI solution that combines large‑model capabilities with traditional data analytics, detailing its product features, AI‑enabled opportunities and challenges, the underlying NL2SQL model, technical architecture, performance optimizations such as materialized views, open APIs, and several enterprise deployment cases.
This article presents Kuaishou's data platform and BI system, describing its evolution from traditional reporting to AI‑driven intelligent analytics, the challenges of diverse user needs and data quality, and the controllable, trustworthy, and feasible solutions that enable large‑scale smart BI deployment.
This article details the purpose, technical evolution, architecture, data‑source unification, dimensional modeling, data‑warehouse layering, SQL‑as‑metric approach, and interactive design of a measurement platform built to improve R&D efficiency through systematic data collection and visualization.
This article details the purpose, technical evolution, data governance, data modeling, warehouse layering, SQL‑based metric definition, and interactive design of Zhuanzhuan's measurement platform, illustrating how systematic data collection and visualization improve R&D efficiency, accuracy, and decision‑making.
The article recounts Firefly Thinking's early BI system limitations, the decision‑making process that led to adopting Volcano Engine, subsequent operational strategies to unlock tool potential, and a forward‑looking vision of data analysis in the large‑model era.
This article outlines Didi's approach to metric management, covering background, data product overview, and challenges in traditional and agile BI models, and presents a comprehensive solution for metric standardization, logical modeling, quality assurance, unified consumption, and future roadmap to improve data warehouse efficiency and consistency.
This article outlines the design, architecture, and practical applications of a quality inspection data collection system, covering data point structures, reporting mechanisms, compliance analysis, intelligent strategy iteration, and BI dashboards, illustrating how big‑data techniques enable digital transformation of inspection processes.
This article explains how to construct a financial indicator system and its underlying data warehouse using FineBI, covering metric hierarchy design, ODS/DWD/DWS/ADS layers, AI‑enhanced BI analysis, and practical tips for organizational adoption and governance.
This article presents JD Logistics' one‑stop agile BI platform, detailing the complex data sources, rapid business demands, the UData solution architecture, performance and usability improvements, and future upgrade plans that together enable faster data integration, self‑service reporting, and enhanced decision‑making across the organization.
This guide introduces DataEase, an open‑source BI platform built with SpringBoot, Apache Doris, and Kettle, walks through its architecture, provides step‑by‑step Docker‑based installation, and demonstrates how to create datasets, visualizations, and dashboards from Excel and MySQL sources.
Sugar BI, evolving from the internal ShowX platform to versions 2.0‑4.0, now offers a zero‑code, drag‑and‑drop visual editor, support for over 30 data sources, AI‑powered automatic analysis and the Sugar Bot Q&A module that transforms multi‑day data tasks into minutes, delivering containerized SaaS BI with intelligent chart recommendation and rapid, code‑free decision‑making for enterprises.
This article details the evolution of Baidu's Sugar BI platform, highlighting its AI‑driven analytics, extensive data source support, zero‑code visual design, smart chart recommendation, and the conversational Sugar Bot that transforms natural‑language queries into actionable visual insights.
The article presents Turing Data Analysis (TDA), a self‑service BI platform that replaces fragile traditional pipelines with a unified DWD‑based data model, drag‑and‑drop analytics, multi‑engine query optimization and caching, delivering sub‑10‑second queries on billions of rows, fine‑grained permissions, and rapid dashboard creation, while reporting significant usage growth and outlining AI‑driven future enhancements.
This article presents JD Logistics' one‑stop agile BI platform, detailing the complex data sources, rapid requirement changes, and Chinese‑style reporting challenges it addresses, while outlining the UData solution, product methodology, performance enhancements, and real‑world case studies that demonstrate significant efficiency gains.
This article reviews the historical evolution of business intelligence (BI) data products, analyzes the difficulties of fine‑grained data analysis and product design, presents ABI strategies to overcome these issues, and explores how large language models are creating a new era of generative AI‑driven data analytics.
This article presents a comprehensive exploration of Shusheng Technology's indicator platform, covering industry challenges, three solution modes (Agile BI, Data Warehouse, Headless BI), the SwiftMetrics architecture, product capabilities, a retail case study, and future development plans for real‑time metrics and AI‑driven services.
This article details the architecture, core processes, and module designs of the JiShi BI platform, a self‑built data visualization and analysis system that integrates data ingestion, processing, enhanced AI‑driven analytics, and multi‑dimensional dashboard capabilities to support enterprise decision‑making.
The article details how the BI platform’s bulky, tightly‑coupled filter was refactored into a standalone, generic ‘bi‑filters’ library managed with Lerna and Vue‑CLI, enabling on‑demand imports, independent packaging, and a three‑layer design that improves extensibility, universality, robustness, reduces code size, and simplifies maintenance.
The article details Kuaishou’s KwaiBI business intelligence platform evolution, covering its 1.0 tool‑based implementation, the 2.0 standardized architecture built on an indicator middle‑platform, core processes, data integration, self‑service features, and future directions for self‑service and intelligent analytics.
This article presents a comprehensive case study of Baicaowei's data platform evolution, digital workflow, metric governance, business modeling, and BI insights, illustrating how big‑data technologies and rational architecture simplification empower the fast‑moving consumer goods sector to enhance operational efficiency and decision‑making.
Guandata’s R&D leader outlines how their analytics platform leverages Delta Lake and Spark to deliver fast, ACID‑compliant BI and AI workloads, detailing architecture, key features like schema evolution and time travel, and practical performance tricks such as compaction, vacuuming, and multi‑engine integration.
This article explains Douyin Group's large‑scale data applications, introduces the concept and architecture of data preparation, details its four subsystems and modular capabilities, and showcases how these are applied in BI, CDP, and custom scenarios within the Volcano Engine ecosystem.
This article summarizes a technical presentation on how Delta Lake is integrated into a BI+AI platform, covering the product background, data‑lake architecture, Delta Lake features such as ACID transactions, schema management, multi‑engine support, performance optimizations, and future development directions.
This article explains the data query fundamentals of YouShu BI charts, introduces intelligent caching design, describes query merging and various optimization techniques—including partition filters, value acceleration, and SQL generation—and outlines performance diagnosis methods to improve BI chart responsiveness.
This article presents an in‑depth overview of the YouShu BI product, outlines the high‑concurrency performance challenges faced by enterprise BI, and details the multi‑layer performance architecture—including front‑end, back‑end, data engine, and data source layers—along with smart caching, MPP acceleration, materialized views, and the Data Doctor operations that together ensure low‑latency, reliable analytics for large‑scale users.
The article reviews DataEase, a Chinese open‑source business‑intelligence platform that offers a low‑learning‑curve interface, extensive data‑source support, built‑in template marketplace, and Docker‑based one‑command installation, making data visualization and dashboard creation accessible to a broad range of users.
This article presents a comprehensive overview of YouShu BI's materialized view product, detailing performance pain points of traditional BI, the fundamentals and architecture of database materialized views, the product's design, ETL generation, query rewriting, scheduling, intelligent recommendation, and future development directions.
The article details Zhongyuan Bank's end‑to‑end agile BI platform construction, covering business goals, a step‑by‑step development timeline, core architecture, eight key functionalities, low‑code data processing, real‑time streaming, visualization dashboards, intelligent Q&A, and future directions for platform intelligence and openness.
The article details HuoLala’s internally built YunTai BI platform, covering its motivation, system architecture, data source integration, zero‑code modeling, visual report and dashboard creation, performance optimizations, and future plans for stability and code design, illustrating a comprehensive big‑data visualization solution.
This article traces the evolution of the modern data stack, explaining the shortcomings of traditional data warehouses, the rise of cloud‑native ELT, self‑service analytics, analytics‑as‑software, and enhanced decision intelligence, while highlighting emerging trends and practical implementations.
This article details how Kuaishou’s Data Application Factory tackles the challenges of rapid BI delivery, data accuracy, and service stability by leveraging low‑code development, unified query services, standardized configurations, and service isolation to achieve efficient, high‑quality data products across multiple business lines.
This article reviews the evolution of data visualization from early charts to modern interactive graphics, analyzes major visualization categories and schools of thought, discusses technical challenges and emerging web and graphics technologies, and highlights the role of visualization in big‑data analytics and BI solutions.
This article traces the origins and evolution of the modern data stack, explains the shortcomings of traditional data warehouses, describes how cloud‑native ELT, modular components and analytics‑as‑software enable self‑service BI and AI, and outlines emerging trends such as DataOps, enhanced analytics and decision intelligence.
This article presents a comprehensive overview of the 58 Agile BI platform called Starfire, covering its background, technical architecture, core permission and query engine challenges, MPP cache acceleration, visualization resource library, developer services, and future development directions.
This article details Huolala's journey from identifying the need for a fast, secure, and scalable BI solution to designing and implementing a self‑service data analysis platform that integrates diverse data sources, offers intuitive visualisation, and addresses real‑world operational challenges.
This article details Xiaomi's multi‑year journey in building a group‑wide Business Intelligence platform, covering its historical evolution, technical challenges in performance, modeling, visualization and permissions, the current four‑layer architecture, and future plans to make the platform more business‑centric and simpler.
An open‑source BI platform built on SpringBoot and Vue offers a visually striking, drag‑and‑drop dashboard editor, supporting multiple data sources like MySQL, Elasticsearch, and Kudu, with 17 ready‑made components, enabling rapid screen design through simple steps of data source configuration, SQL dataset creation, and layout assembly.
This article analyzes the limitations of traditional BI multi‑dimensional analysis that relies on wide tables and complex SQL JOINs, introduces a new DQL language that redefines JOIN operations into three plus one patterns, and demonstrates how DQL simplifies data modeling, reduces errors, and enables truly self‑service analytics.
This article details Sugar BI’s end‑to‑end technical architecture, covering its AI‑enhanced visual analytics, zero‑code report building, multi‑source data integration, smart chart recommendation, and voice interaction, while illustrating the underlying big‑data and cloud components that power the platform.
This article details Qunar's multi‑year journey of designing and evolving a full‑stack BI platform—covering data ingestion, storage, query engines, self‑service analytics, and real‑time OLAP—by iterating through three development phases, selecting technologies such as Impala, Kudu, ClickHouse and Apache Druid, and addressing performance, usability and governance challenges to empower business users with fast, reliable data insights.
The article explains the challenges of big‑data analytics, introduces pre‑computation OLAP concepts, and details how Apache Kylin together with Spark, Flink, Presto and other components can be integrated into a BI platform to achieve near‑real‑time query performance on massive datasets.
This article introduces DataEase, an open‑source BI platform built with SpringBoot, Apache Doris, and Kettle, explains its system and functional architecture, provides step‑by‑step installation commands and configuration details, and demonstrates how to create datasets, views, and dashboards for data analysis.
Metabase is a fast, open‑source BI tool that can be set up in about five minutes using Docker, offering a user‑friendly interface for creating dashboards, custom SQL queries, data pivots, and multi‑table analysis without requiring deep SQL knowledge, making data visualization accessible to all.
This guide walks you through installing the open‑source BI platform DataEase, explains its architecture built on SpringBoot, Apache Doris, and Kettle, and demonstrates how to create data sources, datasets, and visual dashboards from Excel and MySQL using Docker containers.
This article details the evolution, architecture, and technical choices of Qunar's BI platform—from early one‑stop reporting to a modular, self‑service system supporting real‑time analytics, multi‑metric calculations, and unified data governance—highlighting challenges, solutions, and performance benchmarks across big‑data technologies.
This article details Baixin Bank’s multi‑dimensional analysis framework, covering the bank’s business model, data accuracy, completeness and usability requirements, the design of indicator and analysis systems, ladder‑style service concepts, user‑product‑enterprise scenario modeling, and the implementation of self‑service data products and governance processes.
The article details NetEase Yanxuan's end‑to‑end data product ecosystem and data middle platform, describing four core data products, the architecture of the data middle platform, efficient high‑quality delivery, governance practices, and key performance metrics that support data‑driven decision making.
This article explains the design, features, and technical implementation of Youzan’s BI platform, covering its target users, visualization workflow, supported chart types, filtering, permissions, drill‑down, calculated fields, SQL generation logic, and future development directions.
This article provides a detailed, end‑to‑end description of Laiye Technology's BI ecosystem, covering its background, development stages, data acquisition, transmission, transformation, loading, modeling, storage layers, statistical analysis, real‑time metrics, visualization, and future challenges, illustrating how the company builds a scalable, cloud‑native data‑driven platform.
This article details how Alibaba’s Data Experience team builds a comprehensive stack—from a high‑performance SQL editor and low‑code BI platform to sophisticated data visualizations, intelligent analytics, digital art, Web3D rendering, and robust data‑security measures—showcasing the architecture, design principles, and future directions of their end‑to‑end data‑driven product ecosystem.
This article demonstrates how to scrape BI engineer job listings from Zhaopin.com using Python, clean and enrich the data, and create visual analytics with FineBI to reveal salary trends, city distribution, experience levels, and education requirements in the Chinese job market.
This article describes AutoHome's data warehouse architecture, detailing its background, business pain points, layered model design (RDM, ADM, GDM, FDM, Stage/BDM, DIM, TMP), advantages in performance, cost, efficiency, quality, and various application scenarios including BI, analytics, and decision support.
This article details how to use Python to crawl user and article data from the Jianshu platform, then apply FineBI for business intelligence analysis and visualizations, covering author contracts, follower distribution, popular articles, and engagement metrics.
The article explains why companies are building data middle platforms, defines what a data middle platform is, and details NetEase Yanxuan’s architecture, including its data warehouse, data services, and BI platform, illustrating how these components enable data‑driven transformation and fine‑grained operations.
Drawing on NetEase Yanxuan’s experience, this article explains what a data middle platform is, why companies are building one for digital transformation and fine‑grained operations, and details its core components—including the data warehouse, data services, and BI platform—illustrated with real‑world diagrams.
This article explains core data‑warehouse concepts—including DB, DW, ODS, OLTP, OLAP, BI, and the differing meanings of DM—as well as their relationships, integration examples, and why OLAP cannot replace data mining, providing a concise reference for beginners in data analytics.
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.
JD.com’s overseas data analysis platform, dubbed the Columbus platform, combines a lightweight data warehouse deployment with standardized, customizable BI tools to provide real‑time and offline analytics, visualization, KPI management, and future self‑service reporting and predictive capabilities for its global e‑commerce operations.
This article chronicles iQIYI’s journey from a simple Hive‑based data pipeline to the sophisticated, multi‑engine “Tongtian Tower” platform, detailing the development of the Magic Mirror system, the Gear workflow manager, BabelBD, the Monet visual analytics tool, and the integrated BI ecosystem that now supports billions of daily users.
This article explains how Snowflake’s cloud‑native data‑sharing capabilities let users across companies securely access shared datasets without costly extraction or duplication, transforming traditional data‑warehouse and BI workflows and unlocking fresh, detailed insights for businesses.
This article introduces the open‑source BI platform Redash, outlines its wide‑range data source support and visualization capabilities, and provides step‑by‑step Docker‑Compose installation instructions along with practical usage tips for building dashboards.
This article explains why BI is essential for big data platforms, outlines the value hierarchy of data, details the Hadoop‑based analysis workflow, and provides step‑by‑step guidance for constructing both pure Hadoop and hybrid Hadoop‑Spark analytics architectures.
The article explains how a data analyst can use statistical and machine‑learning models such as linear regression, tree‑based boosting, STL decomposition, and Prophet for both non‑time‑series forecasting and time‑series monitoring, highlighting data‑quality concerns, feature‑engineering practices, and deployment considerations like PMML packaging.
At the QCon 2023 Beijing conference, Lianjia's big‑data architect Li Xiaolong presented a comprehensive overview of the company's data platform, detailing its data taxonomy, application scenarios, multi‑layer architecture, and future roadmap for creating a data‑driven real‑estate service ecosystem.
Meituan‑Dianping’s Dashiang Cube is a multi‑source BI reporting platform that unifies MySQL, Kylin, Elasticsearch and plain‑text data via a common SQL layer, generates dialect‑specific queries, performs custom back‑tracking inner and left outer joins across heterogeneous sources, supports scripted metric calculations, permission controls, and a reusable UI component library for self‑service reporting.
The article explains Gartner’s definitions of Business Intelligence and Artificial Intelligence, compares their core capabilities, discusses whether AI is over‑hyped, and illustrates how AI‑driven solutions differ from traditional BI across marketing, public safety, finance, and education sectors.
SQL Server 2017 introduces AI integration, cross‑platform support, Python and R machine‑learning capabilities, stronger data‑layer security, and enhanced BI tools, offering enterprises smarter data management and more flexible, powerful analytics across diverse environments.
This article surveys twelve leading open‑source big‑data and analytics platforms—including Hadoop, Spark, Talend, and MongoDB—detailing their capabilities, market adoption, and how they fit into modern enterprise data‑driven workflows.
This article introduces dataplay2, an open‑source self‑service BI platform built with Flask, pandas, scikit‑learn on the backend and React, ECharts, D3, and other JavaScript libraries on the frontend, detailing its architecture, installation steps, core features such as data upload, visualization, classification, clustering, and future improvement ideas.
This article explains what big data is, where its value lies, how to design and build a big data platform, and the essential steps to turn massive data into actionable business insights while addressing technical and operational challenges.
