Grafana 8.0 introduces a major overhaul of its alerting system, new visualizations such as state timeline, history and histogram panels, reusable library panels, fine‑grained access control, real‑time streaming, enhanced Loki log navigation, and expanded tracing support, all aimed at faster, more flexible observability.
This article compares eight Redis visualization tools—desktop clients, a web app, and an IDE plugin—detailing their features, pricing, platform support, and usability, while also showing how command‑line tricks can enhance JSON handling, helping developers choose the most efficient solution for their workflow.
This tutorial shows how to use Python libraries such as itchat, jieba, matplotlib, snownlp, and Tencent Youtu to fetch WeChat friend data and produce visual analyses of gender distribution, avatar face detection and tags, signature word clouds and sentiment, and geographic location, all illustrated with charts and code examples.
The CSIG Visualization and Visual Analysis Committee’s visit to Alibaba’s Xixi Campus on May 21, 2021 brought together leading academics and industry experts to discuss graph data, big‑data research, spatio‑temporal data, low‑code design, and cutting‑edge visualization techniques, fostering deep industry‑academia collaboration.
This article presents a systematic, data‑model‑driven approach to Kubernetes stability assurance, detailing the sources of complexity, a four‑diagram and three‑table data model, insight and pre‑plan structures, global visualisation concepts, deployment patterns, operational workflows, and competitive analysis to enable effective, iterative, and sustainable cluster stability management.
Cyberbrain, a Python debugging tool created by a Tsinghua alumnus and Google engineer, records detailed data flow, variable changes, and program states, then visualizes them, enabling developers to debug more efficiently without manually stepping through code.
The article presents a comprehensive guide to Python's built‑in functions, standard libraries, and third‑party packages across file I/O, web scraping, databases, data cleaning, statistical analysis, machine learning, visualization, and more, rating each with stars and offering a free e‑book collection for readers.
This article explores the concept of 2.5D graphics, explains why it remains valuable for industrial internet applications, compares CSS+JS, SVG and Canvas implementations, presents code samples and component designs, and details how Alibaba Cloud’s Digital Space project adopted Canvas and Konva to build low‑cost, high‑performance visualizations for industry IoT.
This article explains how to implement a rectangular treemap for outpatient disease data using JavaScript, covering layout algorithms such as Slice‑and‑Dice and Squarified, color filling, and canvas event handling with detailed code examples and performance evaluation metrics.
This article introduces the fundamentals of user profiling, explains tag types and their business value, and demonstrates a data‑driven e‑commerce case study that analyzes gender, age, region, marital status, education, profession, product preferences, purchase timing, and price sensitivity to guide targeted promotion strategies.
Meituan evaluated open‑source distributed graph databases against strict latency, scale, and import criteria, selected NebulaGraph for its superior multi‑hop query and bulk‑load performance, and built a four‑layer, highly available platform that ingests petabyte‑scale data in real time, supports diverse business use cases, and provides interactive visualization.
This article analyzes a dataset of over 5,000 Douyin influencers, exploring gender balance, likes, fan counts, comments, shares, content categories, geographic distribution, graduation schools, verification status, and bio word clouds, and provides full Python code for each visualization.
The article presents a three‑stage pipeline that automatically transforms Sketch design drafts into a DSL view tree, infers layout and style properties, and generates Meituan‑compatible static front‑end code, while offering visual tools for layer cleanup, component recognition, and property adjustment to ensure high‑fidelity mobile UI reproduction.
This article details the technical design and implementation of an IDC digital‑twin platform, covering business requirements, a three‑dimensional interactive framework built on React and ThreeJS, component architecture, user interaction, visual rendering, performance optimizations, and future development plans.
Suning’s data middle platform integrates an accelerated OLAP engine, a star‑schema metrics system, a standardized visualization tool, and a unified report portal to break data silos, enhance security, and transform traditional enterprises into technology‑driven businesses.
This article evaluates eleven popular Redis visualization tools—desktop clients, web applications, and IDE plugins—detailing their features, platform support, pricing, and pros and cons, while providing URLs and screenshots to help developers choose the most efficient solution for debugging and monitoring.
Filelight, an open-source KDE disk-usage visualizer, can be installed on any Linux distribution using the native package manager (pacman, apt, dnf, zypper), then launched to display colorful radial maps of filesystem space, with options to scan specific folders, adjust settings, and exclude virtual directories.
This guide walks through obtaining the nginx-gui tool from GitHub, editing its configuration to point to a local Nginx installation, running the application, and accessing the visual management interface at http://localhost:8889/ with default admin credentials.
This article introduces the Python library communities, which detects and visualizes community structures in graph‑clustering problems, supports multiple algorithms such as Louvain and Girvan‑Newman, provides code examples for importing algorithms, building adjacency matrices, visualizing results with color coding, and creating animation GIFs of the clustering process.
This article walks through acquiring Tianjin bus line data via the Gaode Map API, cleaning and converting geographic coordinates, visualizing station distributions with matplotlib and Baidu maps, and then applying complex‑network analysis to reveal degree distributions, clustering coefficients, and small‑world characteristics of the city’s public‑transport system.
Suning’s Data Middle Platform integrates an accelerated OLAP engine, a star‑schema metric system, a visualization tool built on standardized dimensions, and a unified report portal to solve data silos, improve security, and enable enterprises to evolve into technology‑driven organizations.
LogicFlow, born from DiDi's intelligent middle‑platform, provides a flexible, SVG‑based flowchart editor with rich APIs, MVVM architecture, and extensibility that lets front‑end developers quickly build and customize workflow visualizations while outperforming competing libraries in rendering speed.
This article explains how DevOps evolution intertwines with the surge of internet services, outlines its three conceptual phases, and demonstrates how digital visualization—both terminal‑focused and scenario‑focused—enhances organizational efficiency, quality, and decision‑making during enterprise digital transformation.
This article walks through a full Python‑based sales analysis for a cosmetics company, covering data loading, cleaning, aggregation, visualization, and business‑focused conclusions such as product demand cycles, regional performance, and customer retention strategies.
This article introduces the Python library CuteCharts, explains how to install it, outlines its common configuration options, and provides detailed code examples and visual demos for Bar, Line, Pie, Radar, and Scatter charts that mimic a hand‑drawn style.
This article explains how the popular Python tqdm library can turn opaque, long‑running scripts into transparent processes by adding customizable progress bars in console, Jupyter notebooks, and even nested scenarios such as file downloads, complete with code examples.
This article introduces three powerful visual regular‑expression tools—iHate Regex, the Regular Expression Visualizer & Simulator, and regex101—explaining their features, usage, and how they help developers master pattern matching without fear.
Using Python, this tutorial demonstrates how to scrape 1,585 cherry product listings from Taobao, extract key fields, clean and transform the data, and visualize regional sales and price distributions, highlighting the most popular provinces, stores, and price ranges, all with reproducible code.
This article explains the fundamentals of software architecture diagrams, outlines the purpose and classification of different diagram types, describes common pitfalls, and provides a step‑by‑step guide to using the C4 model—including system context, container, component, and class diagrams—plus a real‑world case study.
Using publicly available statistics from the National Bureau of Statistics, this article visualizes and analyzes China’s divorce rates over the past decade, comparing national trends, provincial variations, and city-specific patterns such as Beijing versus Shanghai, revealing a steady increase and regional disparities.
This article evaluates and compares a range of Redis visualization tools—including desktop clients, web applications, and IDE plugins—by examining their features, usability, platform support, pricing, and command‑line integration to help developers choose the most suitable solution for their workflow.
This article introduces AntV X6, a versatile JavaScript graph‑editing engine, covering its core capabilities such as node and edge creation, custom HTML/React nodes, rich connection styles, grid and background options, interactive tools like ports, grouping, selection, undo/redo, and provides links to tutorials, demos, and extensibility mechanisms.
This guide explains how to install Kibana 7.6.2 on a Linux server, configure its kibana.yml settings, start the service, troubleshoot common permission issues, and access the web interface for visualizing Elasticsearch data in real-time.
The article explores how AI and machine learning are reshaping front‑end development, covering React 17's new JSX transform, Alibaba's AI‑driven front‑end initiatives, graphics programming showcases, AI knowledge‑graph resources, ML‑based static code analysis, and AntV's visualization tools.
This article surveys current macro‑technology trends, covering the democratization of programming through low‑code platforms, Rust’s expanding role in big‑data and machine‑learning, the rise of code‑centric visualization tools, the maturation of infrastructure‑as‑code practices, and how browsers have unintentionally become full‑featured application platforms.
Sampler is a lightweight tool that lets you execute shell commands, visualize their output, and set up alerts using simple YAML configurations, enabling real‑time monitoring of databases, message queues, deployment scripts, and remote servers without requiring a full‑blown monitoring stack.
This article explores how product managers can use abstract thinking to create clear, high‑level product architecture diagrams that clarify direction, support technical and operational planning, and communicate complex ecosystems and business models in a simple visual form.
Sampler is a lightweight tool that runs shell commands, visualizes their output, and can trigger alerts; configured via simple YAML, it works on macOS, Linux and Windows, supports various components such as runcharts, sparklines, gauges, and interactive shells for monitoring databases, queues and system metrics.
The article recounts five years of Youku’s OTT front‑end evolution, detailing the challenges of remote‑control navigation, the design of a component‑tree and focus‑engine architecture, and the progressive shift from raw source development through templating to a visual‑building platform that boosted efficiency, quality, and user engagement.
This article introduces a curated collection of lesser‑known Python libraries for data‑science tasks—including wget, pendulum, imbalanced‑learn, flashtext, fuzzywuzzy, pyflux, ipyvolume, dash, and gym—detailing their purpose, installation commands, and concise code examples to help practitioners expand their toolkit.
This guide presents seven practical techniques—from using Pandas Profiling and Cufflinks‑Plotly visualizations to mastering IPython magic commands, Jupyter formatting, keyboard shortcuts, multiple outputs, and live slide conversion with RISE—to accelerate and enrich everyday data analysis workflows.
This article introduces a curated selection of lesser‑known Python packages—such as wget, pendulum, imbalanced‑learn, FlashText, fuzzywuzzy, PyFlux, ipyvolume, Dash, and Gym—detailing their installation commands, core functionalities, and code examples to help data scientists expand their toolkit beyond the usual pandas, scikit‑learn, and matplotlib.
This article demonstrates how to use Python to scrape Meituan for Shenzhen barbecue restaurant data, clean and enrich the dataset with Pandas, and visualize market insights such as regional distribution, average spending, ratings, and shop types using pyecharts and Matplotlib.
This article provides a detailed comparison of eight widely used Redis visualization tools—including desktop clients, web applications, and IDE plugins—covering their features, UI quality, pricing, command‑line integration, support for Redis data types such as streams, and overall suitability for developers.
The Champion Chasing Boy team presents a comprehensive AI‑driven solution for the 2020 Digital China Innovation Contest, detailing the competition background, data sources, feature engineering, LightGBM modeling, trajectory‑matching algorithms, system architecture, visual analytics, and safety monitoring for maritime vessel activity classification.
This tutorial demonstrates how to convert PDF admission lists into Excel, extract and filter candidate information using Python, compute score segment statistics, and generate bar and pie charts with pyecharts for clear visual insight into graduate enrollment data.
This article walks through a Python‑based exploratory analysis of a public heart‑disease dataset, loading the data, describing its 14 clinical features, visualizing gender, age, heart‑rate, blood‑pressure and cholesterol relationships, and presenting correlation insights to help understand patterns of disease prevalence.
This article explains why visualizing CNN models is crucial for understanding and debugging deep learning systems, outlines three main visualization approaches—basic architecture, activation‑based, and gradient‑based methods—and provides step‑by‑step Keras code examples, including model summary, filter visualization, occlusion mapping, saliency maps, and class activation maps.
This article introduces thirteen popular MongoDB graphical tools—including Robo 3T, Navicat, Compass, NoSQLBooster, ClusterControl, NoSQLClient, Studio 3T, Mongo Management Studio, Aqua Data Studio, phpMoAdmin, Mongotron, Mongolime, and mongo‑express—detailing their key features, platform support, and download links to help users choose the best visual interface for database administration.
Sampler is a lightweight tool that lets you execute shell commands, visualize their output, and set up alerts—all via a simple YAML configuration, making it ideal for on‑the‑fly monitoring of databases, message queues, deployments, and remote servers.
This tutorial walks through a complete Python workflow for analyzing a month of Taobao mobile app data, covering data import, cleaning, feature engineering, traffic and behavior metrics, funnel conversion, RFM segmentation, and visualizations to reveal user patterns and business performance.
This article explains the Open Application Model (OAM), its core concepts of Components, Application Configuration and Traits, and demonstrates a visual platform that lets users create, configure, version, and publish a multi‑component application (nginx, tomcat, redis) on Kubernetes with detailed screenshots and a GitHub demo repository.
This tutorial demonstrates how to acquire, clean, and analyze online restaurant review data using Python, then create customized bar and pie charts with pyecharts, while also illustrating the use of Python's Counter for rating distribution analysis.
This article explains the purpose and benefits of software architecture diagrams, defines key concepts such as architecture and diagram types, outlines common pitfalls, introduces the 4+1 and C4 modeling approaches, and provides practical guidance and examples for producing clear, audience‑focused diagrams.
This tutorial explains how to install and use the Jieba segmentation library and the WordCloud package in Python to process Chinese text, customize dictionaries and stopwords, and generate visually appealing word cloud images based on a mask picture.
The article describes how Alibaba's XiaoMi AI platform constructs a closed‑loop pipeline—from data collection and annotation to model training, evaluation, and real‑time deployment—using multi‑dimensional data processing, visualization, and Spark‑based engines to accelerate iterative improvements and address operational pain points.
This article explains how Alibaba’s XiaoMi team constructs a full‑cycle AI pipeline—covering real‑time and offline data processing, high‑dimensional visualization, model training, iterative feedback, and Spark‑based deployment—to accelerate intelligent product iteration while addressing common engineering pain points.
This article introduces the design and implementation of a web data‑tracking and analysis platform, covering its four‑module architecture, code‑based and visual tracking methods, collected event and user fields, visualization dashboards, and a practical e‑commerce search‑flow case study.
This article walks through downloading World Bank GDP and growth‑rate datasets, explores income‑group classifications, visualizes top‑ranking economies, maps worldwide GDP distribution, and compares growth trends across countries using Python and data‑visualisation techniques.
This tutorial walks you through using Python to crawl Tmall lingerie product and comment data, store it in MySQL, clean and analyze cup‑size distribution and popular colors, and finally visualize the results with pyecharts.
This article compares five JavaScript diagramming libraries—DHTMLX, JointJS, Rappid, yFiles for HTML, Google Charts, and GetOrgChart—by examining their features, documentation, and user experience to help developers choose the right tool for creating online organizational charts.
This tutorial walks you through discovering a real‑time earthquake data source, analyzing its pagination API, building a Python scraper with requests, parsing the JSON responses, storing the results in CSV, and performing visual analyses such as top‑magnitude locations, monthly frequency, repeat‑site counts, magnitude distribution, and word‑cloud generation.
This article demonstrates how to collect, process, and visualize thousands of user reviews for the Chinese animated film "Nezha" from Maoyan, covering dynamic data extraction, JSON parsing, and insightful visual analysis with Python libraries.
This guide introduces the Duc tool for visualizing Linux disk usage, covering its supported interfaces and databases, step‑by‑step installation on Debian‑based systems and from source, core commands for indexing and querying, and how to generate terminal and graphical visualizations.
This guide introduces the open‑source Duc tool, explains its supported interfaces and database back‑ends, provides step‑by‑step installation instructions for Debian‑based and source builds, and demonstrates core commands for indexing, querying, and visualizing disk usage on Linux.
This article explains how to build a comprehensive monitoring system for distributed applications by classifying monitoring functions, describing data quadrants, outlining core modules such as collection, processing, feature extraction, and visualization, and reviewing typical implementations for metrics, logs, tracing, alerting, and the key open‑source components involved.
This article explains a whimsical comic that visualizes the Linux kernel as a multi‑layered house, detailing the foundation of file cabinets, the process table, key system processes like init, cron, Apache and SSH, the role of TTY terminals, and related Linux concepts.
This article demonstrates how to extract the character relationships from the classic novel Romance of the Three Kingdoms using Python, building entity dictionaries, constructing a co‑occurrence social network with HarvestText and NetworkX, visualizing subgraphs, ranking important figures, detecting communities, and creating an animated view of the network’s evolution over the story.
This tutorial walks you through importing Pandas, understanding its Series and DataFrame structures, loading CSV data, inspecting, filtering, indexing, reshaping, merging, visualizing, and finally saving datasets, providing a comprehensive foundation for scientific Python data analysis.
This tutorial shows how to install Graphviz on Linux, write simple DOT files, generate PNG images with the dot command, and use core node, edge, and graph attributes—including subgraph clusters—to produce professional diagrams without a GUI.
This article analyzes the evolving demands of modern databases, outlines essential features for remote management tools, compares open‑source and cloud‑based solutions, and evaluates Tencent Cloud’s console, DMC, and integrated phpMyAdmin, highlighting strengths, shortcomings, and future development directions.
SpriteJS is a Canvas‑oriented rendering library that mimics the DOM API to simplify high‑performance visualizations, offering features such as CSS styling, Flex layout, animation, cross‑platform support, and seamless integration with modern frameworks like React and Vue.
This article walks through the theory of k‑nearest‑neighbors, explains feature selection and normalization, shows how to implement the algorithm with plain JavaScript classes, visualizes results on an HTML canvas, and discusses practical limitations and extensions.
The article explains how visualizing both service delivery and service metrics transforms large‑scale operations into agile, data‑driven DevOps practices, emphasizing automated packaging, continuous delivery, and unified dashboards to improve efficiency, consistency, and fault‑tolerance.
This article crawls Lagou.com to collect 4,500 Python‑related job postings across ten roles, extracts salary, education, experience and skill requirements, visualizes the data with treemaps, rose charts, bar charts and word clouds, and provides detailed insights into each position’s market demands and compensation trends.
This article demonstrates how to crawl Qunar’s nationwide tourism listings with Python, extract key fields such as name, level, location and price, handle anti‑scraping measures, and then perform comprehensive data analysis and visualisation—including sales rankings, popularity scores, geographic distribution and geocoding using the Amap API.
IMWebConf 2018 gathered nearly 500 on‑site attendees and over a thousand online viewers for a deep dive into front‑end standards, WebXR, QUIC, WePY, mpvue, fast‑app, cross‑platform frameworks like Weex and Taro, Node.js performance, and cutting‑edge data visualization techniques.
This article introduces a three‑layer structured thinking framework—understanding, reconstruction, and presentation—explaining how to define problems with SCQA, find entry points using MECE, validate hypotheses via a pyramid structure, and visualize solutions with the Structure Compass.
This article provides a comprehensive guide to decision tree algorithms, covering their theoretical background, classic use‑cases, scikit‑learn's DecisionTreeClassifier parameters, step‑by‑step Python examples for training, visualizing, and exporting trees, as well as a comparison of ID3, C4.5, and CART methods with their advantages and limitations.
DeepInsight is a distributed, micro‑service‑based platform that provides end‑to‑end data exposure, multi‑dimensional visual analysis, and interactive debugging for TensorFlow models, turning opaque neural networks into transparent, controllable systems through real‑time visualizations, dynamic data sets, and integrated lifecycle management.
This article details Minsheng Bank's journey in consolidating fragmented IT operations tools into a single visual platform—integrating CMDB, monitoring, automation, and change data—to improve daily monitoring, fault isolation, change impact analysis, and knowledge sharing while paving the way for AIOps.
This article surveys the most useful open‑source Python packages for data science, covering core numerical libraries, visualization tools, machine‑learning frameworks, natural‑language‑processing kits, and data‑mining utilities, while showing GitHub contribution metrics and Google‑trend popularity.
This article outlines the five fundamental aspects of big data analysis—visualization, data‑mining algorithms, predictive analytics, semantic engines, and data quality management—and explains four primary analytical approaches: descriptive, diagnostic, predictive, and prescriptive analysis.
Using Python to scrape over ten thousand Maoyan comments for the comedy film “The Billionaire” (西虹市首富), this article demonstrates data cleaning, geographic heat‑maps, city‑wise rating analysis, word‑cloud generation, and a simple box‑office forecast based on a comparable movie, illustrating practical web‑scraping and data‑mining techniques.
This tutorial introduces Pandas fundamentals, covering installation, data structures, importing CSV files, inspecting and reshaping data, filtering with boolean masks, indexing, applying functions, grouping, merging, quick plotting, and saving results, all illustrated with clear examples and images.
This article demonstrates how to use Python, pandas, and Jupyter Notebook to explore a comprehensive World Cup dataset, clean and enrich the data, visualize win and goal statistics for all teams, and finally predict the top three contenders for the 2018 tournament.
The article describes Overwatch, a distributed monitoring system developed by Dada‑JD Daojia that collects, aggregates, and visualizes RPC traffic in real time using consumer‑side agents, Kafka, Storm, and a Node.js CQRS architecture, enabling engineers to quickly locate and resolve service failures.
To tackle Meituan‑Dianping’s massive POI and user data challenges, the team built a hybrid 3NF‑plus‑dimensional data warehouse with ODS, BAS, FACT, and optional TOPIC layers, added Kylin‑based pre‑computation cubes, a plug‑in middle‑platform service (metric dictionary, rule engine, compute engine), and interactive ECharts visualizations, delivering unified metrics, faster ad‑hoc queries, scalable analytics, and reduced development costs.
This tutorial walks through analyzing Python job postings scraped from Zhaopin, covering data extraction from MongoDB, cleaning, salary distribution analysis, top city rankings, and generating word clouds of required skills, with complete Python code and visualizations.
This article surveys the most essential Python packages for data science in 2017, covering core scientific computing, data manipulation, visualization, machine learning, deep learning, natural language processing, and web scraping, and explains why each library remains indispensable for modern analysts.
This step‑by‑step guide shows beginners how to use the GUI‑driven KNIME platform to import, clean, visualize, and model the BigMart sales dataset, enabling accurate retail sales predictions without writing any code.
Overwatch is an open‑source distributed monitoring platform built by Dada‑Jingdong Home that collects, aggregates, and visualizes RPC traffic across thousands of micro‑services in real time, enabling engineers to quickly pinpoint the root cause of system failures using directed‑graph visualizations and CQRS‑based data queries.
This article explains what statistics entails, outlines its core tools and methods—including data collection, processing, analysis, and visualization—describes essential software and programming skills, and explores career routes for statisticians and data analysts in both technical and business roles.
This article reviews the most essential Python libraries for data science—including NumPy, SciPy, Pandas, Matplotlib, Seaborn, Bokeh, Plotly, Scikit‑Learn, TensorFlow, Keras, NLTK, Gensim, Scrapy and Statsmodels—highlighting their core features, GitHub activity and typical use cases.
This article walks through the fundamentals of InfluxDB as a time‑series database, demonstrates how to write data via its HTTP API, and shows how to configure Grafana for real‑time visualization, high‑availability deployment, and alerting in a big‑data monitoring scenario.
This article surveys the most essential Python packages for data science, covering core scientific libraries, visualization tools, machine‑learning frameworks, natural‑language‑processing kits, and data‑mining utilities, with brief descriptions and links to each project.
This article presents a series of data‑science questions and expert answers covering overfitting, experimental design for user behavior, the distinction between tall and wide data, detecting chart junk, outlier detection methods, extreme‑value theory for rare events, recommendation‑engine fundamentals, and techniques for visualizing high‑dimensional data.
Ctrip’s rapid internet growth forced a massive data‑center expansion, prompting the company to evolve from self‑built facilities to hybrid vendor‑leased IDC, and ultimately to a visual management platform that automates monitoring, space planning, device intake, and operational workflows, dramatically improving efficiency and reducing manual effort.
The article describes how YHD's precision recommendation team built a real‑time, highly available, traceable general recommendation platform, detailing its background, overall architecture, visual configuration and traceability subsystems, and reporting significant improvements in development speed, reuse and user satisfaction.
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.
