How Obie Insurance cut its month-end close from 25 hours to 2 with dbt State and a lake house rebuild
Blog post from dbt
Obie Insurance’s two-person data engineering team rebuilt its fragmented startup-era data platform into a single dbt project after facing duplicated logic, inconsistent version control, scattered orchestration tools, and daily-only pipeline runs. The company adopted a lakehouse architecture using Apache Iceberg and Snowflake catalog-linked databases, while also testing dbt Lake Compute to balance performance and cost across providers. Comprehensive model documentation, created through YAML files and dbt-osmosis, improved an internal Slack AI bot’s ability to diagnose pipeline failures. dbt State enabled more frequent builds while avoiding unnecessary recomputation, with careful handling of volatile clock-based SQL by moving time-dependent fields into views. The modernization produced a 99% production build success rate, approximately 40% lower costs through model reuse, and a reduction in revenue operations month-end close time from more than 25 hours to about two hours.
No tracked trend matches for this post yet.
Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.