Big Data visualization for Games using Elastic stack
Learn how to generate, process, and visualize Game Dev logs using Elastic Stack with an example of Unreal Engine 5
Big Data visualization for Games using Elastic stack udemy course
Learn how to generate, process, and visualize Game Dev logs using Elastic Stack with an example of Unreal Engine 5
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 Learn
Throughout 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.

