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。

Read how we built Grab's single source of truth for fare storage and management. In this post, we explain how we used the Event Sourcing pattern to build our fare data store.

Read to find out how Grab's Performance Marketing team leveraged on automation to improve conversion rates.

了解Grab如何分析和减少Golang项目的服务可执行文件的大小。

Find out how a team at Grab used Mirror Cache, an in-memory local caching solution, to serve driver-partners data efficiently.

Find out where the messages and rewards come from, that arrive on your Grab app. Walk through scaling and processing optimizations that achieve tremendous throughput.

你有没有想过，当你叫车回家时，点击确认呼叫按钮会发生什么？这个简单的动作背后，发生了很多事情，如果要把所有的事情都说出来，要花上几天几夜的时间。也许，我们可以把这个问题重新表述得更精确一些。你有没有想过Grab是如何存储和使用司机位置来为你分配司机的？如果有，你一定会发现这篇文章很有趣，因为下面会介绍这背后的工作原理。