Deploy and maintain data pipelines and workloads with Azure Databricks
At a glance
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Master the complete lifecycle of building, deploying, and maintaining production-ready data pipelines in Azure Databricks—from design and orchestration to monitoring and optimization.
By the end of this learning path, you'll be able to:
- Design and implement robust data pipelines using notebooks and Lakeflow Spark Declarative Pipelines
- Create and orchestrate Lakeflow Jobs with triggers, schedules, and error handling
- Apply version control and deploy pipelines across environments using Git and Declarative Automation Bundles
- Monitor, troubleshoot, and optimize data workloads for reliability and performance
Prerequisites
- Good understanding of Azure Databricks workspaces
- Familiarity with data engineering concepts and SQL
- Experience with Python programming and notebooks
- Knowledge of Git version control fundamentals
Achievement Code
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Modules in this learning path
Learn to design and implement robust data pipelines in Azure Databricks using notebooks and Lakeflow Spark Declarative Pipelines, covering orchestration, error handling, and task logic.
This module guides you through the process of implementing Lakeflow Jobs in Azure Databricks. You will learn how to create jobs, configure triggers and schedules, set up alerts, and manage automatic restarts to ensure reliable data pipeline execution.
Azure Databricks integrates with established development practices through Git folders for version control and Declarative Automation Bundles for infrastructure-as-code deployments. This module explores Git version control best practices, branching and pull request workflows, comprehensive testing strategies, and CLI-based bundle deployment across environments.
Monitoring and optimization are essential for running reliable, cost-effective data workloads in Azure Databricks. This module explores cluster consumption metrics, Lakeflow Jobs troubleshooting, Spark job diagnostics, performance optimization for caching, skew, spill, and shuffle issues, and log streaming to Azure Log Analytics.