Airflow vs StreamSets: What are the differences?
Introduction
Airflow and StreamSets are both popular data integration and orchestration tools used in the big data and data engineering domain. While both tools have similarities in terms of features and functionality, there are several key differences that set them apart.
1. Airflow: Task-based Workflow Orchestration
Airflow follows a task-based workflow orchestration approach. Jobs or tasks are defined as individual units of work, and dependencies between tasks are explicitly defined. Airflow provides a flexible and powerful scheduling capability, allowing users to schedule tasks based on time, data availability, or event triggers. This task-based approach gives users fine-grained control over dependency management and enables complex workflow design.
2. StreamSets: Data Flow Orchestration
StreamSets, on the other hand, follows a data flow orchestration approach. It focuses on the movement and transformation of data streams rather than individual tasks. Users define data pipelines using connectors and processors, where data flows from one stage to another, undergoing transformations along the way. StreamSets offers a visual interface for designing and monitoring data flows, making it easier for users to construct complex ETL pipelines.
3. Airflow: Python-centric Workflow Definition
Airflow uses Python as its primary workflow definition language. Users define tasks and workflows using Python code, which allows for maximum flexibility and customization. Airflow provides a rich set of operators and hooks for interacting with various data sources and systems. Python code can be used to implement custom functionality and logic, making it a popular choice among developers and data engineers.
4. StreamSets: GUI-based Pipeline Design
StreamSets takes a graphical approach to pipeline design. Users can visually assemble pipelines by connecting pre-built stages and configuring their properties. The graphical interface provides a user-friendly way to design, test, and monitor pipelines without requiring extensive coding or programming skills. StreamSets also supports scripting and expression language for more advanced use cases, but the emphasis is on visual design.
5. Airflow: Plug-and-play Integration with External Systems
Airflow offers seamless integration with a wide range of external systems and tools. It provides a rich set of connectors and hooks for interacting with various databases, cloud services, message queues, and more. Airflow can easily pull data from and push data to different systems, enabling users to design complex workflows involving multiple data sources and destinations.
6. StreamSets: Built-in Data Quality and Data Governance
StreamSets puts a strong emphasis on data quality and data governance. It provides built-in data drift and anomaly detection capabilities, allowing users to monitor data streams for any unexpected changes. StreamSets also includes data lineage and metadata management features, which help users track the origin of data and ensure its reliability and compliance. These built-in quality and governance features make StreamSets a great choice for organizations with strict data integrity requirements.
In Summary, Airflow and StreamSets differ in their workflow orchestration approach, with Airflow focusing on task-based workflows and StreamSets on data flow orchestration. Airflow utilizes Python for workflow definition, while StreamSets offers a visual interface for pipeline design. Airflow has extensive integration capabilities, while StreamSets emphasizes data quality and governance.