Data is the foundation of Grab and the key ingredient to a whole host of fascinating problems the company is addressing, from improving the accuracy of ETA estimates to delivering insight to drivers and operations teams on the ground. Hundreds of dashboards and reports are generated every day to serve decision-making and operations processes.
Grab processes most of its data in batch. While this methodology has served the company well, as the size of both its business and data grows exponentially, there is an increasing demand for a higher availability of data with lower latency and higher quality. Andreas Hadimulyono discusses the challenges that Grab is facing with the ever-increasing volume and velocity of its data and shares the company’s plans to overcome them.
Andreas Hadimulyono is a data warehouse engineer at Grab, where he ensures uninterrupted, error-free uptime while meeting the SLA requirements of business intelligence, analytics, and data science workloads. Previously, Andreas worked for Human Longevity Singapore, where he was responsible for the data pipeline for phenotypic data, which is used for genotype and phenotypes association studies.
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