Mastering Data Ingestion on AWS: A Comparison of Step Functions and Apache Airflow

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Introduction: Data ingestion is a crucial component of any data lake strategy, and selecting the right orchestrator to manage this process is essential for building a scalable, efficient, and maintainable data pipeline. This blog post will compare two popular orchestrators, AWS Step Functions and Apache Airflow, and discuss their use in managing data ingestion workflows on AWS. We will also briefly mention the use of ECS for running Docker containers and shell scripts in the context of data ingestion.

AWS Step Functions for Data Ingestion:

1.1 Overview

AWS Step Functions is a serverless workflow service that enables you to build and manage complex, multi-step applications. It integrates seamlessly with other AWS services, making it an excellent choice for orchestrating data ingestion workflows within your AWS environment.

1.2 Key Features

  • Serverless architecture: No infrastructure to manage, and you pay only for the transitions between steps.
  • Visual workflow editor: Design, modify, and visualize your workflows using a graphical interface.
  • Built-in error handling: Retry failed steps, catch exceptions, and create custom error handlers.
  • Integration with AWS services: Easily connect to other AWS services such as Lambda, ECS, and S3.

Apache Airflow for Data Ingestion:

2.1 Overview

Apache Airflow is an open-source workflow management platform that allows you to programmatically author, schedule, and monitor data pipelines. It provides a rich set of features and a vast ecosystem of plugins, making it a popular choice for managing data ingestion workflows.

2.2 Key Features

  • Extensible and modular architecture: Customize and extend Airflow with plugins and custom operators.
  • Directed Acyclic Graph (DAG) based execution: Ensure tasks run in the correct order and handle dependencies.
  • Rich web-based UI: Monitor and manage your workflows, view logs, and troubleshoot issues.
  • Scalable: Run Airflow on a single node or scale out to a multi-node setup using Celery or Kubernetes.

3. Running Docker Containers and Shell Scripts with ECS:

While discussing data ingestion orchestrators, it is worth mentioning that AWS Elastic Container Service (ECS) can be used to run Docker containers, and Docker containers can run shell scripts. This approach enables you to package and run your data ingestion tasks as containerized applications, making them portable, easy to manage, and scalable.

4. Comparing Step Functions and Airflow for Data Ingestion:

  • Integration with AWS Services Step Functions offers tighter integration with AWS services, making it easier to build data ingestion workflows within the AWS ecosystem. Airflow, however, requires additional setup and configuration to integrate with AWS services.
  • Scalability and Cost Step Functions is serverless and automatically scales with your workload, while Airflow requires manual scaling and infrastructure management. The cost model for Step Functions may be more attractive for certain workloads, but Airflow offers more control over infrastructure costs.
  • Flexibility and Customization Airflow provides more flexibility and customization options with its extensible architecture and plugin ecosystem. Step Functions, on the other hand, offers a more opinionated approach and fewer customization options.

5. Example Use Cases:

AWS Step Functions:

A retail company wants to ingest and process data from multiple sources such as sales transactions, customer information, and inventory. They are already using AWS services for other parts of their infrastructure. In this case, Step Functions would be an excellent choice because of its seamless integration with AWS services, serverless scaling, and visual workflow editor. The company can create workflows to ingest data from various sources, preprocess and transform the data, and store it in their data lake for further analysis.

Apache Airflow:

A data analytics firm needs to build a flexible and customizable data ingestion pipeline that can handle various types of data sources and formats. They require the ability to add new data sources quickly and want to have full control over the infrastructure. In this scenario, Apache Airflow would be an ideal choice due to its extensible and modular architecture, customizable operators, and scalable infrastructure. The firm can create DAGs to orchestrate the ingestion of data from different sources, process and transform the data as needed, and store it in their desired data storage solution.


In conclusion, both AWS Step Functions and Apache Airflow are powerful orchestrators for data ingestion workflows. The right choice will depend on your specific needs, such as integration with AWS services, scalability requirements, and customization options. Assessing your project’s requirements and carefully weighing the pros and cons of each orchestrator will help you select the most suitable solution for your data ingestion needs.

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