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Guidance for Deploying a Serverless, Scalable Game Analytics Pipeline on AWS

A sample infrastructure for getting started quickly with Game Analytics, built by AWS for the game development community.

Getting Started Source Code

References

Reference guide for the guidance's API, event schema, repository files, and operational dashboard components

Upgrading

List of changes from V2 to v3 and step-by-step guide to upgrading your deployment to v3

Component Deep Dive

Step-by-step breakdown of the guidance components and end-to-end analytics process through the pipeline

Customizations

Extending your Game Analytics Pipeline with Dashboards, custom queries, and additional functionality

Design Considerations

Explanations for choosing the services, architecture design, and processes for this guidance

Troubleshooting

List of issue scenarios and steps to troubleshooting them

Made by Daniel Lee, Nathan Yee, Matthew Kwan, Christian Orellana, Rene Roldan, and Steve Parker

Special thanks to Andrei Muratov, Reed Froehlich, Narendra Gupta and Satesh Sonti

Overview

The games industry is increasing adoption of the Games-as-a-Service operating model, where games have become more like a service than a product, and recurring revenue is frequently generated through in-app purchases, subscriptions, and other techniques. With this change, it is critical to develop a deeper understanding of how players use the features of games and related services. This understanding allows game developers to continually adapt, and make the necessary changes to keep players engaged.

The Game Analytics Pipeline guidance helps game developers to apply a flexible, and scalable DataOps methodology to their games. Allowing them to continuously integrate, and continuously deploy (CI/CD) a scalable serverless data pipeline for ingesting, storing, and analyzing telemetry data generated from games, and services. The guidance supports streaming ingestion of data, allowing users to gain critical insights from their games, and other applications in near real-time, allowing them to focus on expanding, and improving game experience almost immediately, instead of managing the underlying infrastructure operations. Since the guidance has been codified as a CDK application, game developers can determine the best modules, or components that fit their use case, allowing them to test, and QA the best architecture before deploying into production. This modular system allows for additional AWS capabilities, such as AI/ML models, to be integrated into the architecture in order to further support real-time decision making, and automated LiveOps using AIOps, to further enhance player engagement. Essentially allowing developers to focus on expanding game functionality, rather than managing the underlying infrastructure operations.