Case Study

Utilizing Real-Time Analytics and Insights Generation to Improve Game Features and Boost User Engagement

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Industry 

Gaming Industry

Expertise 

AWS Lambda, Amazon S3, Amazon API Gateway, Amazon SQS, Amazon DynamoDB, Amazon MSK

Offerings/solutions 

Boosting User Engagement with Efficient and Secure AWS Data Framework

About the Client

A leading company has been a frontrunner in India’s online gaming industry since 2011. As India’s biggest and most trusted brand, it caters to the country’s growing gaming population with top-of-the-line gaming apps, offering a best-in-class gaming experience.

Highlights

Efficient Data Management

Minimal latency and high availability

Enhanced Reliability and Error Resolution

Dead-Letter Queues (DLQs) and retry mechanisms

Proactive Issue Management and Scalability

Amazon CloudWatch Alarms and Amazon SNS

The Challenge

The client wanted to design and implement a scalable pipeline for real-time event streaming into an Amazon Data Lake using AWS services. The pipeline should efficiently handle data ingestion from various sources, ensuring continuous high availability and rapid processing to facilitate timely insights generation. Additionally, the solution should be capable of adapting to fluctuations in data volume and maintaining robustness in the face of potential system failures or disruptions.

Solutions

• Streaming data through Amazon API Gateway to Amazon SQS.
• Validation and enforcement of JSON format and eventName requirements by API Gateway.
• Two queues for processing events and handling errors via DLQ.
• AWS Transformation Lambda normalizes data and sends it to Amazon SQS.
• AWS Consumer AWS Lambda retrieves and publishes events from Amazon MSK via API Gateway.
• Various AWS Lambda functions handle event processing stages, scaling automatically.
• Managed Streaming for Apache Kafka, which simplifies event streaming.
• Encryption with Secrets Manager.
• Amazon S3 for primary storage of events and metadata.
• Amazon DynamoDB for metadata storage.
• Network isolation and security with Amazon VPC.
• Key management and encryption with Amazon KMS.
• Sensitive data access is secured with Secrets Manager.
• Amazon EC2 manages Kafka topics in Amazon MSK.
• Dead-Letter Queue (DLQ) captures processing errors.
• Retry Mechanism Lambda retries failed events and archives unrecoverable ones.
• Manual Retry Lambda resends failed events.
• Amazon CloudWatch Alarms trigger alerts via Amazon SNS for processing errors.

The Results

Achieving efficient, reliable, and secure data management and analytics with minimal latency, driving business growth and enhancing user experiences.

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