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Game development is a multifaceted journey that stretches from the initial concept to post-launch support and live operations. At the heart of this journey is data - the lifeblood that informs decisions at every stage of a video game's lifecycle. However, managing and analyzing this data can be challenging. The challenges range from the sheer volume and diversity of data, to ensuring the quality of that data, to selecting the right tools for its collection, storage, and analysis. This complexity escalates for games offered as services, where the near real-time analysis of data is critical for enhancing player experience, engagement and monetization.

When developing a live title game, telemetry data will be a critical source of insight. An early decision to make is whether you will leverage an external SDK, a SaaS platform, create your own backend services from scratch, or leverage a solution like the Amazon Web Services (AWS) Game Backend Framework to give you a headstart on developing your own first party solution.  Whatever path you choose, it is important to be thoughtful regarding how you get data from your backend, the insights you'll seek to derive from it, how you'll integrate this data with other datasets, and finally, what changes you'll make (or won't make) to your game as a result of your insights. In this blog post, we want to walk you through a best-practice way to set up your own DIY game analytics using Databricks with the AWS Game Backend Framework.

Accelerating Game Data and Analytics with AWS and Databricks

The Role of AWS Game Backend Framework
The AWS Game Backend Framework is designed to streamline backend management, offering a scalable and secure way to integrate game clients with AWS backend services. It supports a wide array of identity management options, including guest logins and social sign-ins, making it versatile for any game developer's needs. With AWS, developers can effortlessly manage game servers, handle player data, and push updates, laying a solid foundation for building and operating live games.

Leveraging Databricks for Advanced Analytics
Databricks complements AWS by offering a unified data intelligence platform, tailored for developers, analysts and data scientists aiming to build exceptional gaming experiences. Databricks excels in ingesting, processing, and analyzing vast diverse datasets (any data source, any type or frequency) and connects seamlessly to the AWS Game Backend Framework. This synergy allows for the near real-time analysis of player behavior, session metrics, financial transactions, and data beyond the game - such as social media, player feedback and reviews.

Enhancing Game Development with AWS and Databricks
By integrating AWS Game Backend Framework with Databricks, developers unlock insights into player behaviors and game performance. This powerful combination simplifies data management and unlocks advanced analytics capabilities.

Key Advantages:

  • Near Real-Time Data Processing: Make data informed decisions to boost engagement and revenue while your players are still engaged.
  • Scalable Solutions: AWS and Databricks effortlessly scale with your game, ready to tackle the dynamic nature of player engagement.
  • Comprehensive Analytics: From basic analytics to advanced machine learning models, Databricks' data intelligence platform gives you the tools to mitigate player churn, optimize monetization strategies, and hyper-personalize player experiences at a global scale.

Getting Started with AWS and Databricks
This guide will walk you through integrating the AWS Game Backend Framework with Databricks, including:

  • Setting Up the AWS Game Backend: How to deploy and configure the AWS Game Backend Framework to manage player data and game sessions.
  • Data Ingestion into Databricks: Techniques for efficiently importing AWS-generated data into Databricks for analysis.
  • Data Curation: Tips for organizing and preparing your data, making it ready for insightful analysis.
  • Advanced Analytics: Strategies for leveraging Databricks to uncover deep insights into game performance and player behavior.

By the end of this guide, you'll be equipped to leverage AWS and Databricks to transform raw game data into actionable insights and further your game's success.

Setting Up the AWS Game Backend

To expand on the detailed AWS Game Backend setup process based on the GitHub guide, here's a high level quick overview of each section.

  • Custom Identity Component Deployment: This process involves the AWS Cloud Development Kit (CDK) to deploy a scalable and secure system for managing player identities and authentication tokens. It's adaptable, supporting various identity providers, and is crucial for creating a secure gaming environment.
  • Backend Component Templates: These templates offer a starting point for developing serverless or containerized backend services. They exemplify best practices in security, resource management, and observability, helping developers establish a solid backend foundation.
  • Integration with Game Engines: The framework's SDKs for Unreal Engine 5, Unity, and Godot 4 simplify integrating game clients with AWS backend services. This step is essential for managing game data and player interactions effectively.
  • Deploying and Testing: This final step involves deploying backend components and testing the integration with game engines. It ensures that the setup is correct and that the game's backend is ready for development and eventual launch.

This is the resulting deployment architecture for the Custom Identity Component

AWS Game Backend Framework

In addition to the above architecture the backend feature creates a Lambda function & Kinesis stream to leverage for downstream analytics.

For detailed instructions and further guidance, refer to the AWS Game Backend Framework documentation on GitHub. We also recommend working through the workshop for AWS Game Backend!

Data Ingestion into Databricks

Databricks integrates easily with Amazon Kinesis to enable near real-time processing and advanced analytics for streaming data. This overview of their integration explores:

  • Near Real-time Data Processing: Leveraging Databricks with Kinesis allows studios to process and analyze large streams of data in near real-time. This capability is vital for live games by enabling next best offers, the identification of fraudulent activities, cheat detection, session based churn prediction and updating live dashboards.
  • Ease of Scalability and Management: The AWS & Databricks integration simplifies the architecture for streaming data pipelines, making it easier to manage and scale. It eliminates the complexity associated with continuously scaling compute resources, and custom data stacks, by offering a platform approach to game insight generation.
  • Advanced Analytics for Game Telemetry: Specifically for game developers, using Databricks with Kinesis enhances the ability to perform sophisticated analytics on game telemetry data. This enables better understanding player behaviors, optimizing game features, and personalizing player experiences during their game session based on gameplay data. These insights can drive game design decisions, improve player retention, and ultimately increase revenue.

These benefits showcase how the Databricks and Kinesis integration creates as a powerful platform for real-time insight generation and unlocks key use cases by enabling the enhancement of game experiences through detailed telemetry analysis.

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Data Curation

Once you've ingested events into Databricks, you can start curating the data to prepare it for analysis. This involves cleaning and transforming the data to ensure that it's accurate and relevant for your analysis.

We break the JSON structured events into columns and rows. Depending on the events you're capturing, curating these can be done with simple Python or SQL. The cells below handle curating session start events into their own tables. A bonus to this approach is that we can dynamically generate these flattened event tables and leverage delta live tables to generate the streaming code needed to power this pipeline end to end.

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Even with minor transformations, or joining data together, with these curated tables a studio can model engagement KPIs, player behavior statistics and be a baseline to support ML use cases like churn prediction and prevention.

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Now that we have flattened streaming tables we have eliminated the need for complex data transformation while we work to derive insight in the next section.

Advanced Analytics

With the data curated and prepared, you can start analyzing it to gain insights into player behavior, game performance, and other key metrics. Databricks provides a range of data analysis tools, including visualizations, SQL queries, and an optimized machine learning environment to support all the solutions studios will run into. Let's look at a simple real time example from the lyra demo.

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These dashboards show the charts and tables initially needed to better understand game and player behavior. Using the data we've ingested and Databricks other common types of analyses you can be performed. Those include:

  • Player segmentation: Group players based on behavior, demographics and other data points to identify patterns, trends and personalization efforts.
  • Game performance: Analyze game performance metrics such as load times, latency, and frame rate to identify areas for optimization and improve player experience.
  • Player retention: Identify factors that influence player retention, such as engagement levels, progression, player journey and rewards.
  • Monetization Recommendation: Analyze in-game purchases and other revenue streams to identify opportunities for monetization.

Speedrunning Value

Leveraging the AWS Game Backend Framework alongside Databricks offers advantages by streamlining game development and analytics processes. This integration enhances the understanding of player engagement and game performance, facilitating optimized decision-making and fostering growth.

Many major studios are leveraging AWS such as the ones here and many are leveraging databricks like these here.

Wrapping it up

Whether you use a backend managed service or DIY one of your own creation: be thoughtful about how you are going to derive insight from the data it produces, will leverage that insight to improve the player experience. Another important consideration is how you'll disseminate this insight across your studio. Marketing, revenue and operations are data rich but don't forget product, development, operations and community can benefit from this data and insight. Integrating insight into your game is much easier early in development than it is towards the end of development. Ensure you build hooks into your games so you personalize the player experience when you go live.

Never forget: the goal of game data analysis is to ensure the player is having the best experience possible. Focusing on this will increase player enjoyment and engagement. Do it right and you increase revenue and build a foundation for a title that survives long term. Most importantly though you'll delight your players, build a stronger brand and a solid community.

Ready for more game data + AI use cases?

Download our Ultimate Guide to Game Data and AI. This comprehensive eBook provides an in-depth exploration of the key topics surrounding game data and AI, from the business value it provides to the core use cases for implementation. Whether you're a seasoned data veteran or just starting out, our guide will equip you with the knowledge you need to take your game development to the next level.

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