Case Study: JetBlue achieves 99.9% data uptime with dbt Labs

JetBlue eliminates data engineering bottlenecks with dbt

JetBlue, the airline based in Long Island City, NY, was struggling with legacy on-premise data warehousing and transformation tools that couldn’t keep up with rapidly growing data volumes. Its centralized data team was hitting bottlenecks, with analysts dependent on data engineers for access, modeling, and data quality work, while compliance and security requirements added even more complexity. To modernize its analytics workflow, JetBlue turned to dbt Labs and Snowflake.

With dbt Labs and Snowflake, JetBlue migrated 26 data sources and 1,200 models in three months, added 6,300 data quality tests, and introduced SQL best practices and documentation to support a more collaborative, governed workflow. The new stack helped eliminate 6- to 8-hour maintenance windows, drive 99.9% data warehouse and pipeline uptime, and improve transparency, testing, and self-service access for analysts.

JetBlue

Benjamin Singleton

Director of Data Science & Analytics