-
EngineeringStructured Logging: The Best Friend You’ll Want When Things Go Wrong
This blog post describes how we built a structured logging framework that integrates well with our existing Elastic stack-based logging backend, allowing us to do logging better and more efficiently.
-
EngineeringHow We Simplified Our Data Ingestion & Transformation Process
This blog post describes how Grab built a scalable data ingestion system and how we went from prototyping with Spark Streaming to running a production-grade data processing cluster written in Golang.
-
Data ScienceUnderstanding 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.
-
EngineeringA Lean and Scalable Data Pipeline to Capture Large Scale Events and Support Experimentation Platform
This blog post focuses on the lessons we learned while building our batch data pipeline.
-
EngineeringDesigning Resilient Systems: Circuit Breakers or Retries? (Part 2)
Grab designs fault-tolerant systems that can withstand failures allowing us to continuously provide our consumers with the many services they expect from us.
-
EngineeringQuerying Big Data in Real-time with Presto & Grab's TalariaDB
In this article, we focus on TalariaDB, a distributed, highly available, and low latency time-series database that stores real-time data. For example, logs, metrics, and click streams generated by mobile apps and backend services that use Grab's Experimentation Platform SDK. It "stalks" the real-time data feed and only keeps the last one hour of data.
-
EngineeringDesigning Resilient Systems: Circuit Breakers or Retries? (Part 1)
Grab designs fault-tolerant systems that can withstand failures allowing us to continuously provide our consumers with the many services they expect from us.
-
EngineeringOrchestrating Chaos Using Grab's Experimentation Platform
At Grab, we practice chaos engineering by intentionally introducing failures in a service or component in the overall business flow. But the failed’ service is not the experiment’s focus. We’re interested in testing the services dependent on that failed service.
