IT Operations Automation · Real-time Analytics
A fragmented, self-managed observability stack forced engineers to jump between tools, left monitoring on the same infrastructure it monitored, and drove high AWS cost and incident blind spots.
Reported impact
DataHub eliminated over $100K in annual AWS observability cost, reduced median incident duration by about 80%, and created one searchable telemetry source that also powers AI-assisted diagnosis and reporting.
DataHub replaced four self-managed observability systems with Observe by Snowflake so logs, metrics, traces, and alert context could be searched from one resilient platform. The team deployed the Observe agent across 22 EKS and GKE clusters and more than 70 isolated customer namespaces, then created separate U.S. and EU tenants through Terraform for regional data handling. Telemetry that previously lived in OpenSearch, Prometheus, Thanos, Grafana, and Alertmanager now lands in a single place where engineers can investigate incidents without switching tools or waiting for another team to build dashboards. DataHub also connected Observe’s CLI and OPAL query language to an internal AI agent in Slack, combining live telemetry with context from incident.io, Argo CD, and internal reference material. That design turns one telemetry store into both a human observability console and an AI-ready context layer for daily reports, faster diagnosis, and future automated remediation.
Telemetry from DataHub's Kubernetes clusters is collected by the Observe agent and centralized in Observe by Snowflake. OPAL queries executed through a Slack-connected AI agent combine Observe data with incident.io and Argo CD context to answer operational questions and generate daily reports.
DataHub chose Observe by Snowflake and brought its telemetry into one platform.
Engineers can ask operational questions in Slack, and the agent can generate and execute OPAL queries
The team deployed the Observe agent across its Kubernetes fleet
cut median incident duration by about 80%
retired over $100,000 a year of AWS infrastructure
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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