Building an Analytics Pipeline for Games

This course addresses how to create an analytics pipeline for game development use cases. Learners will explore common approaches for implementing batch and near real-time analytics and see how they can attain different speeds of insights through a comprehensive analytics solution. Learners will also see how a pipeline can be expanded as their needs change or evolve.

Note: This course includes one or more optional self-paced labs hosted at amazon.qwiklabs.com. Labs must be paid for with credits, which cost $1 USD per credit. Labs typically cost up to 15 credits. This cost is not included with free digital training on aws.training. You will need to create a Qwiklabs account to purchase credits and take a lab.

Intended Audience

This course is intended for:

• Business leaders
• Game developers
• Other game industry professionals

Course Objectives

In this course, you will learn to:

• Describe the key stages of an analytics pipeline and their role.
  Describe how to architect a batch processing and analytics pipeline for games.
• Describe how to architect a near real-time analytics pipeline for games.
• Describe options to integrate batch and real-time pipelines in a unified solution.
• Identify ways in which an analytics pipeline can be augmented or extended to derive additional insights.
• Identify resources available to explore building an analytics pipeline on your own.

Prerequisites

We recommend that attendees of this course have:

• Foundational understanding of cloud computing and Amazon Web Services (AWS), equivalent to Cloud Practitioner Essentials certification
• Basic knowledge of data analytics and AWS Analytics services, equivalent to Data Analytics Fundamentals
• Basic understanding of the business case for analytics in games, equivalent to Why Analytics for Games

Delivery Method

• This course is delivered through digital training.

Duration

• 90 minutes

Course Outline

• Module 1: Introduction
  • Course overview
  • Pre-test
  • Review
• Module 2: Stages of an analytics pipeline
  • Analytics pipeline overview
  • Ingesting game data
  • Storing game data
  • Processing and analyzing game data
  • Consuming game data
  • AWS services mapped to pipeline stages
• Module 3: Batch processing and analytics
  • Batch analytics and use cases
  • Architectural patterns for batch analytics
  • Ingesting batch game data
  • Storing batch game data
  • Processing batch game data
  • Analyzing batch game data
  • Consuming batch game data
  • Demonstration: Building a batch analytics pipeline
• Module 4: Near real-time processing and analytics
  • Real-time analytics and use cases
  • Architectural patterns for streaming analytics
  • Ingesting near real-time game data
  • Storing near real-time game data
  • Consuming near real-time data
• Module 5: Putting it all together
  • Reasons for integrating pipelines
  • How to configure data sources
  • Examples of pipelines using batch and speed layers
• Module 6: Extending the pipeline
  • Including additional data sources
  • Enabling Live Ops monitoring
  • Creating event-driven architectures
  • Automating workflows or derive predictive insights with machine learning
• Module 7: Conclusion and next steps
  • Summary
  • Knowledge assessment
  • Resources for additional information and exploration