# Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs

DevFeed: [Data Mesh at Grab (Part III): Operationalizing data reliability with automated DPIs](<https://devfeed.tech/articles/data-mesh-at-grab-part-iii-operationalizing-data-reliability-with-automated-dpis-1247.md>)

Original publisher: [Read original article](<https://engineering.grab.com/data-mesh-at-grab-part-three>)

Author: Harvey Li

Published: 2026-08-28T00:00:00Z

Content type: article

Language: en

Sources: [Grab Tech](<https://devfeed.tech/sources/grab-tech.md>)

Topics: [incident](<https://devfeed.tech/topics/incident.md>), [Kafka](<https://devfeed.tech/topics/kafka.md>)

Tags: [apis](<https://devfeed.tech/tags/apis.md>), [data](<https://devfeed.tech/tags/data.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [database](<https://devfeed.tech/tags/database.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [incident](<https://devfeed.tech/tags/incident.md>), [mesh](<https://devfeed.tech/tags/mesh.md>), [observability](<https://devfeed.tech/tags/observability.md>), [platform](<https://devfeed.tech/tags/platform.md>), [production](<https://devfeed.tech/tags/production.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

## AI overview

Grab describes an automated Data Production Issue lifecycle for maintaining reliable data products. An incident orchestrator checks data-contract health, starts a DPI when contract tests fail, and distinguishes alerts from confirmed contract breaches.

## Source excerpt

Introduction In the first two parts of this series, we described how Grab approaches data mesh through the Signals Marketplace: a way for teams to publish, discover, and reuse trusted data products across domains. Part II introduced the foundational tools behind certification: Hubble for metadata and ownership, Genchi for data quality observability, and the Data Contract Registry for explicit producer-consumer guarantees. Certification is the starting point for a trusted data marketplace. It gives downstream consumers confidence in an asset's ownership, documentation, lineage, and quality controls. Certification does not eliminate runtime failure. A certified table can still arrive late. A certified metric can still be affected by a broken dependency. A certified Kafka stream can still violate a freshness expectation. Keeping certified data products reliable in production requires more than defining standards upfront. Teams need a consistent way to detect failures, diagnose the root cause, fix the issue, and verify recovery. That is where Data Production Issues (DPIs) come in. At Grab, DPIs turn data quality signals into an operational workflow. The DPI lifecycle A good DPI should be clear enough to act on, and it should close automatically when the underlying condition recovers. From the beginning, we designed the DPI lifecycle to be automated, with minimal human-in-the-loop. The lifecycle starts when Kinabalu, Grab's incident orchestrator, observes that a data asset may no longer satisfy its contract. The contract captures the reliability expectations that matter for the asset, along with the health checks, exposed through Test Health application programming interfaces (APIs), that evaluate those expectations. The orchestrator stays decoupled from platform internals. It does not need to know how each platform computes freshness, completeness, or other quality dimensions. It only needs to ask whether the relevant contract tests are healthy. If one or more contract