Driving Southeast Asia Forward with AWS

Summary

Grab transitioned its transportation platform from a single Ruby on Rails monolith on Amazon EC2 and Amazon RDS MySQL to a microservices architecture hosted on Amazon Web Services. The platform processes multi-petabyte real-time data flows and hundreds of millions of GPS data points to match drivers with passengers and push proactive demand heat maps. Operational efficiency is sustained with fewer than ten full-time infrastructure engineers, supported by AWS managed services. The analytics backend also transitioned from MySQL to Amazon Redshift, eventually moving to an Amazon S3 data lake using Amazon EMR and Presto. These data-driven matching systems improved driver-passenger allocation rates by up to 30%.

Takeaways

1. 1

   Leveraging real-time big data mapping increased driver-passenger booking allocation rates by up to 30%.

2. 2

   Fewer than 10 full-time engineers maintain the entire underlying AWS infrastructure despite a 200-fold increase in usage over four years.

3. 3

   The internal analytics architecture evolved across iterations from MySQL to Amazon Redshift, and ultimately to an Amazon S3 data lake running Presto on Amazon EMR.