-
Engineering · Data Science · Product
Building hyperlocal GrabMaps
Being hyperlocal is a key advantage for GrabMaps. In this article we will explain what being hyperlocal means and how it helps GrabMaps bring value to our driver-partners and passengers through the Grab platform. -
Engineering
Streamlining Grab's Segmentation Platform with faster creation and lower latency
Since 2019, Grab's Segmentation Platform has served as a comprehensive solution for user segmentation and audience creation across all business verticals. This article offers an insider look at the platform's design and the team's efforts to optimise segment storage, ultimately reducing read latency and unlocking new segmentation possibilities. -
Data Science · Security
Unsupervised graph anomaly detection - Catching new fraudulent behaviours
As fraudsters continue to evolve, it becomes more challenging to automatically detect new fraudulent behaviours. At Grab, we are committed to continuously improving our security measures and ensuring our users are protected from fraudsters. Find out how Grab’s Data Science team designed a machine learning model that has the ability to discover new fraud patterns without the need for label supervision. -
Engineering · Security
Zero traffic cost for Kafka consumers
Grab's data streaming infrastructure runs in the cloud across multiple Availability Zones for high availability and resilience, but this also incurs staggering network traffic cost. In this article, we describe how enabling our Kafka consumers to fetch from the closest replica helped significantly improve the cost efficiency of our design. -
Engineering
Go module proxy at Grab
While consolidating code into a single monorepo has its benefits, there are also several challenges that come with managing a large monorepo like slow performance and low developer productivity. Find out how Grab’s FLIP team contributes and leverages the open-sourced Athens Go module proxy to improve developer productivity at Grab. -
Engineering · Security
PII masking for privacy-grade machine learning
Data engineers at Grab work with large sets of data to build and train advanced machine learning models to continuously improve our user experience. However, as with any data-handling company, dealing with users' data may present a potential privacy risk as it contains Personally Identifiable Information (PII). Read this article to find out more about Grab’s mature privacy protective measures and how our data streaming team uses PII tagging and masking on data streaming pipelines to protect our users. -
Engineering
Performance bottlenecks of Go application on Kubernetes with non-integer (floating) CPU allocation
At Grab, we have been running our Go based stream processing framework (SPF) on Kubernetes for several years. But as the number of SPF pipelines increases, we noticed some performance bottlenecks and other issues. Read to find out how this issue came about and how the Coban team resolved it with non-integer CPU allocation.

Stepping up marketing for advertisers: Scalable lookalike audience
A key challenge in advertising is reaching the right audience who are most likely to use your product. Read this article to find out how the Data Science team improved advertising effectiveness by using lookalike audiences to identify individuals who share similar characteristics with an existing consumer base.
