Case Study: Shipper detects data incidents in minutes with Databand.ai

Shipper Detects Data Incidents From Days To Minutes With Databand

Shipper, a fast-growing logistics company in Indonesia, needed better visibility into its data pipelines as it expanded its customer-facing dashboards and migrated to a cloud stack with Amazon and Databricks. The team struggled with missed data SLAs, missing or inaccurate dashboard data, changing third-party API schemas, and costly weekly pipeline failures, often only discovering issues after customers were affected.

Shipper implemented Databand.ai’s data observability platform to automate detection of schema changes, missing data sources, and critical pipeline failures, while also integrating alerts into Opsgenie and Jira for faster remediation. With Databand.ai, Shipper cut mean time to detection from three days to minutes, improved mean time to resolution, and gained real-time SLA tracking and better root cause analysis, helping the team keep dashboards reliable and customers happier.

Shipper

Fithrah Fauzan

Data Engineering Lead