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所在平台: Udemy |
课程主页: https://www.udemy.com/course/elastic-for-games/
课程评论：没有评论
课程名称：使用Elastic Stack进行游戏的大数据可视化 概述：欢迎来到《使用Elastic Stack进行游戏的大数据可视化》课程！本课程是掌握游戏开发数据驱动洞察的入门课程，旨在帮助您利用Elastic Stack（ELK）进行游戏数据的处理、分析和可视化。无论您是数据分析师、质量保证工程师、技术负责人、管道架构师、自动化/DevOps工程师还是技术艺术家，本课程都将提供实用技能，以改善开发工作流程和决策。 课程内容：在本课程中，您将探索并实施大数据可视化解决方案，重点关注三种关键的游戏开发日志： 1. 游戏会话数据：跟踪玩家的游戏时间、平台及相关信息，为更具体的指标（如每小时崩溃率、平均游戏会话时长等）提供基础。 2. 性能数据：分析不同版本、平台和游戏场景下的历史性能指标（FPS、CPU/GPU使用率、内存消耗、函数执行时间），以便于在性能优化方面做出明智决策。 3. 地理位置数据：使用Kibana重建玩家移动路径，绘制游戏崩溃、稀有boss击杀、FPS下降等关键事件的互动游戏地图。 通过本课程，您将拥有一个功能齐全的大数据仪表板，将原始日志转化为可操作的洞察！课程强调实践，您将通过各种挑战和研讨会进行学习，而不仅仅是观看幻灯片讲座。我们将使用虚幻引擎5及其样本项目Stack-O-Bot作为游戏日志，模拟真实世界数据和有意义的分析指标。 所有涉及的工具均可免费访问，源代码也会包含在课程内容中。
Welcome to Big Data Visualization for Games using Elastic Stack!This course is your gateway to mastering data-driven insights for game development using the Elastic Stack (ELK).Whether you're a Data Analyst, QA Engineer, Tech Lead, Pipeline Architect, Automation/DevOps Engineer, or a Tech Artist, this course is designed to equip you with the practical skills to process, analyze, and visualize game data for improved development workflows and decision-making.What You'll LearnThroughout the course, you'll explore and implement Big Data visualization solutions, covering three essential types of game development logs:Game Session Data: Track who played the game, for how long, and on which platform, providing insights and foundation for more specific metrics, like Crash-per-hour rate, average play session durations, etc.Performance Data: Analyze historical Performance metrics (FPS, CPU/GPU usage, memory consumption, function execution times) across different builds, platforms, and gameplay scenarios to make informed decisions in performance optimizations.Location-Specific Data: Recreate player movement path, map game crashes, rare boss kills, FPS dropped, and other key events using interactive game maps in Kibana.By the end of this course, you'll have a fully functional Big Data dashboard that transforms raw logs into actionable insights!This course is fully practical (similar to my Python-related courses) where most of the time you're attending workshops with various challenges rather just watching raw-slides lectures. As a source of our game logs throughout the course we will be using Unreal Engine 5 with its Sample Project Stack-O-Bot to mimic the real-world data and meaningful metrics for analysis.All the tools involved in the course content have Free access.Source Code included.
