Udemy
    •  
    •  
    •  
    •  
    •  
    •  
    •  
    •  
Subscribe
Turn what you know into an opportunity and reach millions around the world.
Learn More
Your cart is empty.
Keep shopping
Big Data visualization for Games using Elastic stack
Rating: 5.0 out of 5(4 ratings)
58 students

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
Created byViacheslav Makhynko
Last updated 2/2025
English

What you'll learn

  • Learn how to generate, process and analyze logs created during the game development process with Elastic Stack
  • Utilize Elastic stack (Elasticsearch, Logstash, Kibana) to process game development logs
  • Build Kibana Dashboard with insightful widgets representing Session, Location and Performance game data
  • Integrate Unreal Engine 5 Game World map to Kibana Visualization
  • Use Python to interact with Elasticsearch

Course content

7 sections • 25 lectures • 4h 16m total length

  • Course Introduction8:00

    Explore big data visualization for games with Elastic Stack, generating and visualizing logs from sessions, builds, performance, and location data using Elasticsearch, Logstash, and Kibana.

  • How to use this course2:32

Requirements

  • Skills: No previous experience in the field required
  • Tools: Elastic Stack, Unreal Engine 5, Python, VS Code. All have free access.

Description

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.

Who this course is for:

  • Data Analysts
  • Quality Assurance, DevOps, Automation Engineers
  • Technical/Pipeline Directors
  • Tools programmers
  • Technical Artists

Report abuse