Yash Joshi

American Airlines migrated from Hive Metastore to Unity Catalog using automated processes with Databricks APIs and GitHub Actions. This automation streamlined the migration for many applications within AA, ensuring consistency, efficiency and minimal disruption while enhancing data governance and disaster recovery capabilities.

How do you transform a data pipeline from sluggish 10-hour batch processing into a real-time powerhouse that delivers insights in just 10 minutes? This was the challenge we tackled at one of France's largest manufacturing companies, where data integration and analytics were mission-critical for supply chain optimization. Power BI dashboards needed to refresh every 15 minutes. Our team struggled with legacy Azure Data Factory batch pipelines. These outdated processes couldn’t keep up, delaying insights and generating up to three daily incident tickets. We identified Lakeflow Declarative Pipelines and Databricks SQL as the game-changing solution to modernize our workflow, implement quality checks, and reduce processing times.In this session, we’ll dive into the key factors behind our success: - Pipeline modernization with Lakeflow Declarative Pipelines: improving scalability - Data quality enforcement: clean, reliable datasets - Seamless BI integration: Using Databricks SQL to power fast, efficient queries in Power BI