Case Study: Wood Mackenzie achieves improved collaboration, transparency, and productivity with Databricks Lakeflow Jobs

Workflows enables automation and collaboration in the energy sector

Wood Mackenzie, a consulting and analytics firm serving the energy and natural resources sectors, needed a better way to manage complex ETL pipelines that ingest and clean massive volumes of structured and unstructured data. Before working with Databricks, team members ran notebooks independently, making it hard to see dependencies, identify failing stages, and know which code version had been used, which slowed troubleshooting and collaboration.

Using Databricks Lakeflow Jobs, Wood Mackenzie built a common, automated workflow across Python notebook stages with built-in alerting and clearer visibility into ownership and execution. The result was improved collaboration, transparency, productivity, and data quality, faster issue resolution, and cost savings versus manually running interactive notebooks.

Wood Mackenzie

Yanyan Wu

Vice President of Data