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Engineering
Rethinking Stream Processing: Data Exploration
As Grab matures along the digitalisation journey, it is collecting and streaming event data generated from the end users of its superapp on a larger magnitude than before. Coban, Grab’s data-streaming platform team, is looking to help unlock the value of streaming data at an earlier stage of the data journey before this data is typically stored in a central location (“Data Lake”). This allows Grab to serve its superapp users more efficiently. -
Engineering · Data Science
Kafka on Kubernetes: Reloaded for fault tolerance
Dive into this insightful post to explore how Coban, Grab's real-time data streaming platform, has drastically enhanced the fault tolerance on its Kafka on Kubernetes design, to ensure seamless operation even amid unexpected disruptions. -
Engineering · Security
Championing CyberSecurity: Grab's bug bounty programme in 2023
Since its launch in 2015, Grab’s Bug Bounty programme has made strides in giving back to the global security community and aiding research. Read this article to find out more about our quarterly campaigns in collaboration with HackerOne and other achievements we’ve had in 2023. -
Engineering
Sliding window rate limits in distributed systems
In the field of distributed systems, there are several common challenges, such as rate limiters and fast queries in big data. In this blog post, we delve into how we address these challenges with sliding window rate limits to optimise marketing communications for our users. -
Engineering · Data Science · Product
An elegant platform
Supporting real-time data streaming enables our internal users to build intelligent applications and services, a crucial aspect of continuously out-serving our community. Read this article to understand our journey of building a real-time data streaming platform from pure Infrastructure-as-Code towards a more sophisticated control plane, and the benefits of this solution. -
Engineering · Data Science · Product
Road localisation in GrabMaps
With GrabMaps powering the Grab superapp we have the opportunity to improve our services and enhance our map with hyperlocal data. No matter the use case, road localisation plays an important role in Grab’s map-making process. However, road localisation entails handling a substantial volume of data, making it a costly and time-consuming endeavour. In this article, we explore the strategies we have implemented to drive down costs and reduce processing times associated with road localisation. -
Engineering · Security
Graph modelling guidelines
Graphs are powerful data representations that detect relationships and data linkages between devices. This is very helpful in revealing fraudulent or malicious users. Graph modelling is the key to leveraging graph capabilities. Read to find out how the GrabDefence team performs graph modelling to create graphs that can help discover potentially malicious data linkages. -
Engineering · Data Science
LLM-powered data classification for data entities at scale
With the advent of the Large Language Model (LLM), new possibilities dawned for metadata generation and sensitive data identification at Grab. This prompted the inception of our project aimed to integrate LLM classification into our existing data management service. Read to find out how we transformed what used to be a tedious and painstaking process to a highly efficient system and how it has empowered the teams across the organisation.