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.

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.

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.

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.

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.

After upgrading to Xcode 13.1, we noticed a few issues such as instability of the CI tests and high CPU utilisation. Read to find out how the Test Automation - Mobile team investigated these issues and resolved them by integrating observability tools into our iOS CI development process.

The Talaria open-source project has made significant improvements by replacing Lua VM with the Go plugin resulting in 2.3x faster performance and memory usage reduction. Talaria is a time-series database designed for Big Data systems used to process millions of transactions and connections daily at Grab, requiring scalable data-driven decision-making.

As Flink becomes more popular with real-time stream applications, we realise that Flink deployments are sometimes stressful and prone to errors. The Coban team deep dives into the issues with our existing Flink deployment process, possible mitigations, and the eventual solution to ensure safer deployments of Flink streaming applications.