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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.
I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.
For a non-streaming approach:
You could consider using more checkpoints throughout your spark jobs. Furthermore, you could consider separating your workload into multiple jobs with an intermittent data store (suggesting cassandra or you may choose based on your choice and availability) to store results , perform aggregations and store results of those.
Spark Job 1 - Fetch Data From 10 URLs and store data and metadata in a data store (cassandra) Spark Job 2..n - Check data store for unprocessed items and continue the aggregation
Alternatively for a streaming approach: Treating your data as stream might be useful also. Spark Streaming allows you to utilize a checkpoint interval - https://spark.apache.org/docs/latest/streaming-programming-guide.html#checkpointing
Pros of Airflow
- Features53
- Task Dependency Management14
- Beautiful UI12
- Cluster of workers12
- Extensibility10
- Open source6
- Complex workflows5
- Python5
- Good api3
- Apache project3
- Custom operators3
- Dashboard2
Pros of StreamSets
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Cons of Airflow
- Observability is not great when the DAGs exceed 2502
- Running it on kubernetes cluster relatively complex2
- Open source - provides minimum or no support2
- Logical separation of DAGs is not straight forward1
Cons of StreamSets
- No user community2
- Crashes1
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