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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.

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Apache SparkApache Spark

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.

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Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 267.3K views
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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

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Pros of Airflow
Pros of StreamSets
  • 51
    Features
  • 14
    Task Dependency Management
  • 12
    Beautiful UI
  • 12
    Cluster of workers
  • 10
    Extensibility
  • 6
    Open source
  • 5
    Complex workflows
  • 5
    Python
  • 3
    Good api
  • 3
    Apache project
  • 3
    Custom operators
  • 2
    Dashboard
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    Cons of Airflow
    Cons of StreamSets
    • 2
      Observability is not great when the DAGs exceed 250
    • 2
      Running it on kubernetes cluster relatively complex
    • 2
      Open source - provides minimum or no support
    • 1
      Logical separation of DAGs is not straight forward
    • 2
      No user community
    • 1
      Crashes

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    What is Airflow?

    Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

    What is StreamSets?

    An end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps.

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    What are some alternatives to Airflow and StreamSets?
    Luigi
    It is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.
    Apache NiFi
    An easy to use, powerful, and reliable system to process and distribute data. It supports powerful and scalable directed graphs of data routing, transformation, and system mediation logic.
    Jenkins
    In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.
    AWS Step Functions
    AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly.
    Pachyderm
    Pachyderm is an open source MapReduce engine that uses Docker containers for distributed computations.
    See all alternatives