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Azure Databricks with ADF: Orchestration Patterns
Azure Data Factory can run Databricks notebooks, Python files, Jar files and jobs as pipeline steps. This guide covers the linked service and its cluster options, passing parameters in and reading results back, how the cluster choice changes run time and cost, and the orchestration patterns that hold up in production.
Orchestration: Airflow vs Dagster vs Prefect
Airflow, Dagster and Prefect compared by writing the same real pipeline in each: Airflow's DAG of tasks, Prefect's flows that run like ordinary Python, and Dagster's assets that describe the data to be produced, with the Prefect and Dagster versions run on real taxi data, a comparison table and how to choose.
Workflow Orchestration with Apache Airflow DAGs
Once you can write an Airflow DAG, the next problems are about design: files that slow the scheduler, branches that silently skip the final step, and dozens of near-identical pipelines. This guide runs four DAG design patterns on Airflow 3.3.2 and shows what each one fixes, with real output.
Pipeline Orchestration: Airflow vs Prefect vs Dagster
Machine learning pipelines ask more of an orchestrator than nightly ETL: skipping expensive steps that have not changed, training many segment models with retries, and knowing which model came from which data. This guide runs those needs in Prefect and Dagster on real data, and maps them to Airflow.
Glue Workflows vs Step Functions for Orchestration
Glue workflows and Step Functions can both run a multi-step pipeline on AWS, but one only knows about Glue and the other coordinates almost any service. This comparison covers what each can start, how they handle failure and reruns, how they are triggered and billed, and where managed Airflow fits as a third option.