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Airflow vs Matillion: What are the differences?

Introduction

Airflow and Matillion are two popular tools used for data integration and orchestration. While both serve similar purposes, they have several key differences that set them apart. In this article, we will explore six significant differences between Airflow and Matillion.

  1. Architecture: Airflow follows a code-driven architecture where workflows are defined in Python code, allowing for highly customizable and extensible workflows. On the other hand, Matillion utilizes a graphical user interface (GUI) with drag-and-drop components, making it more user-friendly and easy to use for non-developers.

  2. Scalability: Airflow is a distributed system that can handle the execution of large-scale workflows on multiple machines, allowing for horizontal scaling. Meanwhile, Matillion is designed to run on a single server or virtual machine, limiting the scalability options.

  3. Supported Data Sources: Airflow has a wide range of connectors that support various data sources, including databases, cloud storage, messaging systems, and more. Matillion, on the other hand, natively supports data integration with popular cloud data warehouses like Amazon Redshift, Google BigQuery, and Snowflake.

  4. Built-in ETL Capabilities: Airflow provides basic ETL functionality through its operators and tasks, but it heavily relies on external tools and libraries for data transformations and processing. In contrast, Matillion offers a comprehensive set of built-in ETL capabilities, allowing users to perform complex data transformations within the platform itself.

  5. Cost Structure: Airflow is an open-source framework, which means it is free to use and has no licensing costs. However, users need to set up and manage their own infrastructure, which can incur operational costs. Matillion, on the other hand, is a commercial product with a subscription-based pricing model, which includes support and maintenance.

  6. Learning Curve: Airflow requires users to have a solid understanding of Python and its ecosystem, as workflows are defined using Python code. This can make it a bit challenging for non-developers to get started. In contrast, Matillion's GUI interface makes it easier for users with limited programming knowledge to design and execute workflows, reducing the learning curve.

In summary, Airflow offers more customization and scalability options with its code-driven architecture, while Matillion provides a user-friendly GUI and built-in ETL capabilities. Airflow is suitable for developers and users with programming experience, whereas Matillion is more accessible to non-developers and offers easy integration with cloud data warehouses.

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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 · 265.3K views
Recommends
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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 Matillion
  • 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 Matillion
    • 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 Matillion?

      It is a modern, browser-based UI, with powerful, push-down ETL/ELT functionality. With a fast setup, you are up and running in minutes.

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      What are some alternatives to Airflow and Matillion?
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