@grab-engineering
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As Grab's Data Lake grew to petabytes, the limitations of Hive Parquet became clear, from catalog latency and small files to manual partition management. This blog shares our journey adopting Apache Iceberg as our default table format: why we chose it, how we rolled it out at scale, the tooling w...
Counter Service powers real-time fraud detection at massive scale, handling billions of requests daily. Learn how we migrated its underlying storage with zero downtime, redesigning the writer data model, incorporating clean data access layer separation on the reader side, building dual read-write...
Grab is moving hundreds of services to Distroless images to cut CVEs but only if workloads still run cleanly. Learn how medium tests became the migration gate, where the scaffolding toil stalled the campaign, and how an agentic workflow (skills, MCP, guardrails) generated tests and Docker changes...
How do you actually build a secure execution environment for artificial intelligence (AI) agents? In Part 2, we dive deep into Palana's architecture. Learn how Grab handles proxy-mediated secrets, large language model (LLM) routing, and strict network access under the hood. We also share our oper...
Artificial intelligence (AI) agents are evolving from chat interfaces into autonomous workloads, bringing new security risks. In Part 1, discover why Grab built Palana, a Kubernetes-native platform designed to run AI agents safely. We explore the core design principles behind treating isolation a...
Building on Grab's Docs-as-Code approach, we reflect on our documentation journey, uncovering the benefits, challenges, and limitations along the way. Learn why we made the shift, what we gained in search and quality assurance, and when each approach works best.
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 boos...
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...
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...
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' i...
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 in...
