At Grab, we're transforming data ingestion and processing with Hugo, our self-service data platform. Now integrated with Apache Flink, Hugo empowers teams to build real-time data pipelines effortlessly. Discover how we've streamlined complex processes into a single, one-click experience that boosts productivity and enables rapid insights. Dive into our blog to explore this game-changing evolution!

Frustrated by 35-minute integrated development environment (IDE) syncs? In a large monorepo, slow builds were eroding productivity. Discover how we built a custom Focus plugin to slash sync times to under 2 minutes and drop memory usage from 10 GB to 2 GB. Learn how we leveraged Gradle and Bazel to reclaim developer flow without changing a single team's workflow.

Discover how Grab's data streaming team has revolutionized Apache Flink deployments with Shadow Testing, ensuring seamless reliability for real-time applications. By deploying new versions alongside existing ones without disruption, we eliminate downtime and enhance application availability. Dive into our article to explore this innovative approach and how it boosts deployment confidence and efficiency.

How does Grab manage quality across hundreds of thousands of data assets? Discover the foundational tools powering our Signals Marketplace. We dive into Hubble for discovery, Genchi for observability, and our Data Contract Registry to see how event-driven certification turns 'data as a product' into a reliable, AI-ready reality. Stop guessing and start trusting your data.

The Analytics Data Warehouse (ADW) team at Grab supports over 1,000 users. These users support an extensive repository of more than 15,000 tables. To alleviate the time-consuming demands of repetitive tasks, the team implemented a multi-agent AI system. This system autonomously handles simpler inquiries and collaborates on more complex requests, reclaiming significant engineering bandwidth and unlocking hundreds of hours of productivity each month.

How Grab enabled R8 optimization for its Android app at scale, over 9 million lines of code and more than engineers. Read how we achieved 25% ANR reduction, 16% app size decrease, and 27% faster startup through AI-assisted debugging with MCP tools, pragmatic testing strategies, and optimized feedback loops

In the quest to optimize app performance, managing the image cache was crucial. This blog takes us on a journey from a traditional Least Recently Used (LRU) cache to a Time-Aware Least Recently Used (TLRU) cache. This innovative approach reclaimed terabytes of storage across millions of devices while maintaining user experience and controlling server costs. Discover how Grab's TLRU implementation cleverly balances storage optimization and performance, offering a glimpse into the future of app development.