Category: Engineering

At Grab, we continuously enhance our systems to improve scalability, reliability and cost-efficiency. Recently, we undertook a project to split the read and write functionalities of one of our backend services into separate services. This was motivated by the need to independently scale these operations based on their distinct scalability requirements. In this post, we […]

Introduction At Grab, we are committed to leveraging the power of technology to deliver the best services to our users and partners. As part of this commitment, we have developed the LLM-Kit, a comprehensive framework designed to supercharge the setup of production-ready Generative AI applications. This blog post will delve into the features of the […]

The Databricks Serverless compute infrastructure launches and manages millions of virtual machines (VMs) each day across three major cloud providers, and it is a substantial challenge in operating the infrastructure at this scale with efficiency. Today, we want to share with you some of the work that we have recently done to enable a true […]

Introduction GrabX serves as Grab’s central platform for product configuration management. GrabX client services read product configurations through an SDK. This SDK reads the configurations in a way that’s eventually consistent, meaning it takes about a minute for any configuration updates to reach the client SDKs. However, some GrabX SDK clients, particularly those that need […]

Today we’re excited to announce eBay Evo, eBay’s new brand and design system. eBay Evo represents a significant evolution of eBay, aimed at enhancing the customer experience through a modern and simplified approach, ensuring a consistent global presence.  The launch of eBay Evo marks a significant milestone, representing years of dedicated in-house work to deliver […]

Providence Health’s extensive network spans 50+ hospitals and numerous other facilities across multiple states, presenting many challenges in predicting patient volume and daily census within specific departments. This information is critical to making informed decisions about short-term and long-term staffing needs, transfer of patients, and general operational awareness.  In the early stages of Databricks adoption, […]

Introduction In the initial article, LLM Powered Data Classification, we addressed how we integrated Large Language Models (LLM) to automate governance-related metadata generation. The LLM integration enabled us to resolve challenges in Gemini, such as restrictions on the customisation of machine learning classifiers and limitations of resources to train a customised model. Gemini is a […]

As the season of giving approaches, we at Databricks have been making our list and checking it twice–but instead of toys and treats, we’ve been wrapping up powerful performance improvements for our users. Through analyzing billions of production queries and listening closely to our community’s wishes, we’re excited to deliver a package of enhancements that […]

We’re excited to announce that materialized views (MVs) and streaming tables (STs) are now Generally Available in Databricks SQL on AWS and Azure. Streaming tables offer simple, incremental ingestion from sources like cloud storage and message buses with just a few lines of SQL. Materialized views precompute and incrementally update the results of queries so […]

When serving machine learning models, the latency between requesting a prediction and receiving a response is one of the most critical metrics for the end user. Latency includes the time a request takes to reach the endpoint, be processed by the model, and then return to the user. Serving models to users that are based […]