Data Orchestration with Databricks Workflows

Chapters 00:00 Introduction to Databricks Workflows 02:38 Key features and improvements in Workflows 08:17 Advanced workflow triggers and notifications 15:00 Collaboration features 20:26 Future Developments and conclusion Linkedin: https://www.linkedin.com/in/hubertdudek/ Documentation: https://docs.databricks.com/aws/en/jobs/ Databricks Workflows is a powerful orchestration engine designed to streamline data processing, analytics, and machine learning pipelines on the Databricks Data Intelligence Platform. It enables users to define, manage, and monitor multitask workflows across ETL, analytics, and AI workloads. Below is an overview of its key components and capabilities: AI-Powered Error Diagnosis Diagnose job errors instantly, providing root cause analysis and fixes to keep your workflows running smoothly. Alerts Get notified of task failures, delays or anomalies via email, Slack or custom webhooks to quickly resolve issues and maintain workflow reliability. Repair and Rerun Repair tasks with ease — retry failed nodes intelligently without rerunning successful ones, saving time, reducing costs and boosting efficiency. Modular Orchestration Simplify complex workflows by breaking them into reusable components, improving maintenance, scalability and flexibility across your data pipelines. Data Lineage Track data flow and transformations across workflows, ensuring visibility into dependencies for improved control, debugging and compliance management. CI/CD With Asset Bundles Integrate CI/CD with Databricks by using Asset Bundles to manage resources, automate deployments and simplify testing with environment-specific configurations. Orchestrate SQL Tasks Run SQL queries on Databricks SQL warehouses, allowing you to create, schedule and monitor workflows with Databricks SQL objects like queries and alerts. Power Real-Time Pipelines Process real-time data streams seamlessly, enabling automated workflows that adapt instantly to new events for timely insights and actions.