Given the dominance of the free to play model and in-app purchases offered in the gaming industry, games have become more of a service than a product. With this change, in-game analytics is critical to constantly engage and monetize users to stay competitive.

Amazon Web Services offers a comprehensive suite of analytics solutions to help you keep your players engaged and optimize your game to increase revenue. Amazon Kinesis, a platform for streaming data on AWS, offers powerful services that make it easier to analyze player experiences, advertising effectiveness, and game usage statistics in real-time to improve your user’s experience.

This webpage provides best practices and guidance to consider when implementing a gameplay analytics solution, and introduces an AWS solution that helps customers more easily ingest, store, and analyze gameplay data. The Gaming Analytics Pipeline automatically provisions and configures the AWS services necessary to start consuming and analyzing gameplay data in minutes.

When analyzing gameplay data in the cloud, determine the game design questions you want answered. Then, work backwards to determine the gameplay data you need to answer those questions. With this in mind, consider these best practices when choosing a gaming analytics solution:

  • Choose a solution that can automatically scale to ingest large amounts of data. Elastic resources help reduce the operational complexity of manually provisioning and maintaining resources.
  • Consider how the solution batches records. A solution that batches records into smaller groups can process data more quickly while a solution that batches records into larger batches can be more cost-effective.
  • Plan for scalability, data durability, and fault tolerance for data consumption, processing, and storage.
  • Implement granular access-control policies and use encryption to protect your gameplay data.

AWS offers a solution that automatically launches and configures Amazon Kinesis Streams to ingest gameplay data, Amazon Kinesis Client Library (KCL) consumer applications running on AWS Elastic Beanstalk to process and filter that data, Amazon Simple Storage Service (Amazon S3) to store the data and act as a gateway to other AWS services, and Amazon Redshift to act as a queryable backend data store. The diagram below presents the Gaming Analytics Pipeline architecture you can deploy in minutes using the solution's implementation guide and accompanying AWS CloudFormation template.

  1. Game servers transmit gameplay events to an Amazon Kinesis stream (called the telemetry stream) that collects and processes those events.
  2. An application validates, sanitizes, and enriches the events; archives the events as a batch telemetry file in Amazon S3; and sends a pointer to the location of the batch telemetry file to a separate Amazon Kinesis stream (called the file stream) that initiates the process of loading the data into Amazon Redshift.
  3. A second application loads batches of events from Amazon S3, deletes duplicate events, and inserts events into tables in Redshift.
  4. A third application performs routine database tasks and maintenance.
  5. AWS Elastic Beanstalk is used to manage the deployment of the solution’s three applications.
  6. The solution also includes a data generator you can use to test the pipeline and a heat map generator that allows you to generate heat maps based on various parameters.  
Deploy Solution
Implementation Guide

What you'll accomplish:

Deploy Gaming Analytics Pipeline using AWS CloudFormation. The CloudFormation template will automatically launch and configure the components necessary to consume and analyze gameplay data.

Automatically collect, process, and store gameplay data using Amazon Kinesis Streams, Amazon S3, and Amazon Redshift.

What you'll need before starting:

An AWS account: You will need an AWS account to begin provisioning resources. Sign up for AWS.

A data producer that can call the Amazon Kinesis PutRecord and/or PutRecords operation(s), and produces records in the required format.

Skill level: This solution is intended for IT infrastructure professionals who have practical experience with streaming data and architecting on the AWS Cloud.

Q: What analytics tools and services can I use with this solution?

This solution stores data in Amazon Redshift, which has validated integrations with popular BI and ETL vendors. You can also go to the AWS Marketplace to deploy and configure solutions designed to work with Amazon Redshift in minutes. You can use Amazon Athena to query the data in Amazon S3 or Amazon Redshift Spectrum to query the data in Amazon S3 and Amazon Redshift. You can also use Amazon Quicksight for reporting, ad-hoc queries, and more detailed analysis.

Q: How long after data enters the pipeline is it available for analysis?

The solution writes batches to Amazon S3 every 100MB, 10 minutes, or 500,000 records per shard. The solution refreshes the batch telemetry file pointers every 10 minutes or 36 files. As a result, it can take up to 20 minutes between when event data enters the pipeline to when it is available for analysis.

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