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Data First, SLA Always
Introducing Trailblazer, the Data Engineering team’s solution to implementing change data capture of all upstream databases. In this article, we introduce the reason why we needed to move away from periodic batch ingestion towards a real time solution and show how we achieved this through an end to end streaming pipeline.
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Making Grab’s Everyday App Super
To excel in a heavily diversified market like Southeast Asia, we leverage on the depth of our data to understand what sorts of information users want to see on our Feed and when they should see them. In this article we will discuss Grab Feed’s recommendation logic and strategies, as well as its future roadmap.
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Catwalk: Serving Machine Learning Models at Scale
This blog post explains why and how we came up with a machine learning model serving platform to accelerate the use of machine learning in Grab.
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Tourists on GrabChat!
Just over two years ago we introduced GrabChat, Southeast Asia’s first of its kind in-app messaging platform. Since then we’ve added all sorts of useful features to it such as auto-translated messages, the ability to send photos, and even voice messages! It’s been a great tool to facilitate smoother communications between our driver-partners and our passengers, and one group in particular has found it incredibly useful: tourists!
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Bubble Tea Craze on GrabFood!
Bubble Tea’s popularity on GrabFood has captured our attention and we want to celebrate its fascinating growth with you! We have deep-dived into Grab’s Big Data to find the most interesting bubbles of treasures that can excite your palette. Hopefully these insights can help you understand what’s behind the Bubble Tea craze in GrabFood in Southeast Asia!
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How We Harnessed the Wisdom of Crowds to Improve Restaurant Location Accuracy
We questioned some of the estimates that our algorithm for calculating restaurant wait times was making, and found that the "errors" were actually useful to discover restaurants whose locations had been incorrectly registered in our system. By combining such error signals across multiple orders, we were able to identify correct restaurant locations and amend them to improve the experience for our customers.
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Recipe for Building a Widget: How We Helped to “Peak-Shift” Demand by Helping Passengers Understand Travel Trends
We help to “peak-shift” demand by helping passengers understand travel trends with Grab’s data. Curious to know how we empower our passengers to make better travel decisions? Read on!
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Understanding Supply & Demand in Ride-hailing Through the Lens of Data
Grab aims to ensure that our passengers can get a ride conveniently while providing our drivers better livelihood. To achieve this, balancing demand and supply is crucial. This article gives you a glimpse of one of our analytics initiatives - how to measure the supply and demand ratio at any given area and time.
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Journey of a Tourist via Grab
Grab's services to tourists are an integral part of connecting tourists to various destinations and attractions. Do tourists travel on Grab to outlandishly fancy places like those you see in the movie "Crazy Rich Asians"? What are their favorite local places? Did you know that Grab's data reveals that medical tourism is growing in Singapore? Here are some exciting travel patterns that we found about our tourists' Grab rides in Singapore!
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Grab Senior Data Scientist Liuqin Yang Wins Beale-Orchard-Hays Prize
Grab Senior Data Scientist Dr. Liuqin Yang wins the 2018 Beale-Orchard-Hays Prize, the highest honor in Computational Mathematical Optimization. He has been recognised for his paper and the corresponding software SDPNAL+.
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GrabShare at the Intelligent Transportation Engineering Conference
We're excited to share the publication of our paper GrabShare: The Construction of a Realtime Ridesharing Service, which was Grab's contribution to the Intelligent Transportation Engineering Conference in Singapore last month.
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The Data and Science Behind GrabShare Part I: Verifying Potential and Developing the Algorithm
Launching GrabShare was no easy feat. After reviewing the academic literature, we decided to take a different approach and build a new matching algorithm from the ground up.
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How to Go from a Quick Idea to an Essential Feature in Four Steps
How do you work within a startup team and build a quick idea into a key feature for an app that impacts millions of people? It's one of those things that is hard to understand when you just graduate as an engineer.
