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Databricks — Best Practices in Choosing the Right Workflow Orchestration Framework
Choosing the right orchestration framework for ETL/ELT workflows is crucial to ensuring performance, maintainability, and scalability in production. Databricks provides multiple orchestration solutions, including Databricks Workflows, Delta Live Tables (DLT), and supports external orchestrators like Apache Airflow. Each has its strengths and use cases. In this blog post, we’ll break down the features, pros, cons, and provide examples to guide you in selecting the right tool for your data engineering needs.
1. Overview of Orchestration Frameworks
Orchestration frameworks help manage the flow of tasks in a data pipeline. In ETL/ELT workflows, orchestration ensures that extraction, transformation, and loading steps occur in the correct sequence, handling dependencies, retries, and failures.
Key Orchestration Solutions in Databricks:
- Databricks Workflows: A native orchestrator within Databricks that supports running notebooks, scripts, JARs, and parallel tasks.
- Delta Live Tables (DLT): A declarative pipeline framework built on Delta Lake with automated data quality checks and optimizations.
- Apache Airflow: A popular open-source orchestration tool that integrates well with Databricks and other cloud services.
