Orchestration Tools Projects
Tools for scheduling and orchestrating data workflows.
3 projects available
How to Choose the Right Orchestration Tool for Python?
To choose among Airflow, Luigi, Prefect, Dagster, and Argo Workflows: Airflow is ideal for complex, large-scale workflows needing robust scheduling and monitoring. Luigi is suitable for batch job workflows with linear and straightforward task dependencies. Choose Prefect for modern, cloud-native workflows with a focus on simplicity and flexibility. Dagster is best for data-centric workflows, providing a comprehensive view of data pipelines and assets. Opt for Argo Workflows for container-native environments, especially when orchestrating machine learning pipelines or data processing tasks on Kubernetes.
Daily Order Processing with Apache Airflow
advancedBuild a production-ready data pipeline using Apache Airflow DAGs to process daily orders. Learn to orchestrate complex workflows with multiple operators, implement branching logic, data quality checks, and handle task dependencies in the industry's most widely-adopted orchestration tool.
Pokemon ETL Pipeline with Prefect
beginnerCreate a modern ETL pipeline with Prefect to extract Pokemon data from the PokeAPI, transform it, and load into SQLite. Perfect for learning Prefect's intuitive task and flow decorators with a fun, beginner-friendly example that demonstrates retry logic and error handling.
Stock Market Analysis with Dagster
intermediateBuild a data pipeline with Dagster to fetch stock data from Yahoo Finance, calculate moving averages, and store results in a database. Learn Dagster's functional approach with ops, jobs, and schedules while working with real financial data.