Introduction
Building reliable data pipelines requires more than connecting data sources to destinations. You need to design workflows that handle failures gracefully, scale with growing data volumes, and remain maintainable as business requirements evolve. Azure Databricks provides multiple approaches for creating data pipelines—from flexible notebooks with procedural code to Lakeflow Spark Declarative Pipelines that automate orchestration and data quality enforcement.
When you design data pipelines, you make decisions that affect every downstream consumer of your data. The order of operations determines whether transformations build on validated, well-structured data. Your choice between notebooks and declarative pipelines influences how much orchestration code you write versus how much the platform manages for you. Task dependencies in Lakeflow Jobs control execution flow and enable parallel processing that reduces pipeline runtime.
Error handling separates production-ready pipelines from fragile prototypes. Without proper error handling, invalid records corrupt downstream analytics, unnoticed failures accumulate technical debt, and problems surface hours or days after they occur. Azure Databricks provides built-in mechanisms for data quality expectations, retry policies, and conditional task flows that help you build resilient data workflows.
This module guides you through designing and implementing data pipelines in Azure Databricks. You learn how to structure pipeline operations, choose the right approach for your use case, configure task logic in Lakeflow Jobs, and implement error handling strategies. You also create pipelines using both notebook-based and declarative approaches, gaining hands-on experience with the tools that power production data platforms.