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Airflow

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Airflow vs Xplenty: What are the differences?

<Write Introduction here>
  1. Scalability: Airflow is highly scalable as it allows users to easily scale their workflows by adding more worker nodes, whereas Xplenty has limitations on workflow scalability due to its cloud-based execution model.
  2. Customization: Airflow provides a higher level of customization with the ability to write custom plugins and operators, while Xplenty has a more restricted set of connectors and transformations for data processing.
  3. Community Support: Airflow has a large and active community offering extensive documentation, tutorials, and support forums, whereas Xplenty has a smaller community and limited resources for troubleshooting and assistance.
  4. Ecosystem Integration: Airflow seamlessly integrates with various external services and tools such as Kubernetes, AWS, and Slack, enabling users to build versatile data pipelines, while Xplenty has limited integrations and dependencies on its own platform for data processing.
  5. Dynamic Task Dependency: Airflow allows for dynamic task dependency configuration based on task outcomes and runtime conditions, providing more flexibility in workflow design, whereas Xplenty has a more static task dependency model with limited options for dynamic scheduling.
  6. Cost: Airflow is an open-source project with no licensing fees, making it cost-effective for organizations, whereas Xplenty is a paid service with subscription-based pricing, adding to the operational costs for data processing needs.

In Summary, Airflow and Xplenty differ in terms of scalability, customization, community support, ecosystem integration, dynamic task dependency, and cost.

Advice on Airflow and Xplenty
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AirflowAirflowLuigiLuigi
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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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Replies (1)
Gilroy Gordon
Solution Architect at IGonics Limited · | 2 upvotes · 259.2K views
Recommends
on
CassandraCassandra

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 Xplenty
  • 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
  • 2
    Simple, easy to integrate/process data without coding

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Cons of Airflow
Cons of Xplenty
  • 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
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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 Xplenty?

    Read and process data from cloud storage sources such as Amazon S3, Rackspace Cloud Files and IBM SoftLayer Object Storage. Once done processing, Xplenty allows you to connect with Amazon Redshift, SAP HANA and Google BigQuery. You can also store processed data back in your favorite relational database, cloud storage or key-value store.

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    What are some alternatives to Airflow and Xplenty?
    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