Telematics is a collection of sensor data such as accelerometer data, gyroscope data, and GPS data that a driver’s mobile phone provides, and we collect, during the ride. With this information, we apply data science logic to detect traffic events such as harsh braking, acceleration, cornering, and unsafe lane changes, in order to help improve our consumers’ ride experience.

Typically, modern applications use various database engines for their service needs; within Grab, these would be MySQL, Aurora and DynamoDB. Lately, the Caspian team has observed an increasing need to consume real-time data for many service teams. These real-time changes in database records help to support online and offline business decisions for hundreds of teams.

Because of that, we have invested time into synchronising data from MySQL, Aurora and Dynamodb to the message queue, i.e. Kafka. In this blog, we share how real-time data ingestion has helped since it was launched.

Learn about the challenges of points rewarding and how GrabRewards Points are rewarded for different Grab offerings.

In large organisations, it is a common practice to isolate the cloud resources of different verticals. Amazon Web Services (AWS) Virtual Private Cloud (VPC) is a convenient way of doing so. At Grab, while our core AWS services reside in a main VPC, a number of Grab Tech Families (TFs) have their own dedicated VPC. One such example is GrabKios. Previously known as “Kudo”, GrabKios was acquired by Grab in 2017 and has always been residing in its own AWS account and dedicated VPC.

In this article, we explore how we exposed an Apache Kafka cluster across multiple Availability Zones (AZs) in Grab’s main VPC, to producers and consumers residing in the GrabKios VPC, via a VPC Endpoint Service. This design is part of Coban unified stream processing platform at Grab.

GrabAds is a service that provides businesses with an opportunity to market their products to Grab’s consumer base. During the pandemic, as the demand for food delivery grew, we realised that ads could be a service we offer to our small restaurant merchant-partners to expand their reach. This would allow them to not only mitigate the loss of in-person traffic but also grow by attracting more customers.

Many of these small merchant-partners had no experience with digital advertising and we provided an easy-to-use, scalable option that could match their business size. On the other side of the equation, our large network of merchant-partners provided consumers with more choices. For hungry consumers stuck at home, personalised ads and promotions helped them satisfy their cravings, thus fulfilling their intent of opening the Grab app in the first place!

In recent years, Identity and Access Management has gained importance within technology industries as attackers continue to target large corporations in order to gain access to private data and services. To address this issue, the Grab Identity team has been using a 6-digit PIN to authenticate a user during a sensitive transaction such as accessing a GrabPay Wallet. We also use SMS one-time passwords (OTPs) to log a user into the application.

We look at existing mechanisms that Grab uses to authenticate its users and how biometric authentication helps strengthen application security and save costs. We also look at the various technical decisions taken to ensure the robustness of this feature as well as some key learnings.

In 2016, Clayton Christensen, a Harvard Business School professor, wrote a book called Competing Against Luck. In his book, he talked about the kind of jobs that exist in our everyday life and how we can uncover hidden jobs through the act of non-consumption. Non-consumption is the inability for a consumer to fulfil an important Job to be Done (JTBD).

JTBD is a framework; it is a different way of looking at consumer goals and is based on the notion that people buy products and services to get a job done. In this article, we will walk through what the JTBD framework is, look at an example of a popular JTBD, and look at how we use the JTBD framework in one of Grab’s services.

Learn about the different optimisation techniques when building a search index.

Find out how you can run an automated test and simultaneously roll out a new feature.

Find out how the GrabFood team cut their bundle size by 3 times with these 7 webpack bundle optimisation strategies.

Learn how Grab improves privacy protection to cater to various geographical locations.

At Grab, the Lending team is focused towards building products that help finance various segments of users, such as Passengers, Drivers, or Merchants, based on their needs. The team builds products that enable users to avail funds in a seamless and hassle-free way. In order to achieve this, multiple lending microservices continuously interact with each other. Each microservice handles different responsibilities, such as providing offers, storing user information, disbursing availed amounts to a user’s account, and many more.

In this tech blog, we will discuss what Data and Extract, Transform and Load (ETL) pipelines are and how they are used for processing multiple tasks in the Lending Team at Grab. We will also discuss Ratchet, which is a Go library, that helps us in building data pipelines and handling ETL tasks. Let’s start by covering the basis of Data and ETL pipelines.

Learn how the Marketplace Tech Family debugged and resolved Market-Store's high latency issues.

了解信托、身份、安全和安保（TISS）团队如何利用内部构建的解决方案Archivist改进机器学习预测。

Read more about our long awaited piece on the automation work we have made through integration and hermeticity.

随着聊天的持续增长和新的内部工具的出现，帮助我们的代理更有效率和生产力是确保为我们的用户提供更快的支持时间并进一步扩大聊天规模的关键。

从对另一个第三方工具的使用情况进行分析，以及进行一些观察，我们意识到建立一个基于模板的功能不会有帮助。我们需要提供个性化的功能，因为我们的消费者支持专家关心他们的写作风格和语气，而使用模板往往让人觉得是机器人。

我们决定建立一个机器学习模型，称为SmartChat，它通过利用几个内部数据源提供上下文建议，帮助我们的聊天专家更快地打字，从而为更多的消费者服务。

在这篇文章中，我们将解释从问题发现到设计迭代的过程，并分享该模型是如何从数据科学和软件工程角度实现的。