Summary
This blog post discusses improvements made to the stability of Hugo, a pivotal data ingestion tool at Grab, by implementing automation solutions. The complexities of managing over 4,000 data pipelines revealed challenges such as limited user visibility and overwhelming on-call demands. The article details an architectural solution including modules for signal collection, diagnosis, root cause analysis, auto-resolution, and data health API, aimed at enhancing monitoring and efficiency. The outcome led to better visibility of data health, reduced on-call workload, and scalable solutions for data issue resolution, with future plans to expand automation capabilities.