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Engineering · Data ScienceHow we store and process millions of orders daily
The Grab Order Platform is a distributed system that processes millions of GrabFood or GrabMart orders every day. Learn about how the Grab order platform stores food order data to serve transactional (OLTP) and analytical (OLAP) queries.
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EngineeringHow we automated FAQ responses at Grab
Most frequently asked questions (FAQ) are repetitive, which hinder on-call engineers' productivity. Read to find out how we automated FAQ responses at Grab, allowing engineers to focus on operational tasks.
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Engineering · Security · Data ScienceGraph Networks - 10X investigation with Graph Visualisations
As fraud schemes get more complex, we need to stay one step ahead by improving fraud investigation methods. Read to find out more about graph visualisation, why we need it and how it helps with uncovering patterns and relationships.
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Engineering · Security · Data ScienceHow facial recognition technology keeps you safe
Facial recognition technology has grown tremendously in recent years due to the rise of deep learning techniques and accelerated digital transformation. Read to find out more about facial recognition technology in Grab and the components that help keep you safe.
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Engineering · SecurityGraph concepts and applications
Graph theory-based approaches show the concepts underlying the behaviour of massively complex systems and networks. Read to find out how graphs came about, where they can be used and the part they play in graph technology.
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Engineering · Data ScienceAutomated Experiment Analysis - Making experimental analysis scalable
Analysts and data scientists invest lots of time into creating trustworthy experiments, which are key to making sound decisions. Read to find out how Automated Experiment Analysis helps make experimental analysis more scalable.
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EngineeringSearch architecture revamp
Grab’s search architecture was initially designed to only support exact text matching based on user queries. Find out what problems the Deliveries search team faced and how they improved the search architecture to address these issues.
Engineering
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Data Science
Automatic rule backtesting with large quantities of data
At Grab, real-time fraud detection is built on a rule engine. As data scientists and analysts, we need to analyse and simulate a rule on historical data to check the performance and accuracy of the rule. Backtesting, also known as Replay, enables analysts to run simulations of either newly-invented rules, or evaluate the performance of existing rules using past events ranging from days to months, and significantly improve rule creation efficiency.
