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  5. Airflow vs Matillion

Airflow vs Matillion

OverviewDecisionsComparisonAlternatives

Overview

Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128
Matillion
Matillion
Stacks51
Followers71
Votes0
GitHub Stars0
Forks0

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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Advice on Airflow, Matillion

Anonymous
Anonymous

Jan 19, 2020

Needs advice

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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Comments

Detailed Comparison

Airflow
Airflow
Matillion
Matillion

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.

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.

Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Edit, Transform and Load Data intuitively; Load Data from Dozens of Sources; 50% reduction in ETL development and maintenance effort ; Rich orchestration environment; Work as a team; Cheap; Billing via AWS.
Statistics
GitHub Stars
-
GitHub Stars
0
GitHub Forks
-
GitHub Forks
0
Stacks
1.7K
Stacks
51
Followers
2.8K
Followers
71
Votes
128
Votes
0
Pros & Cons
Pros
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Observability is not great when the DAGs exceed 250
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 1
    Logical separation of DAGs is not straight forward
No community feedback yet
Integrations
No integrations available
Amazon S3
Amazon S3
Zendesk
Zendesk
MongoDB Stitch
MongoDB Stitch
Amazon Redshift
Amazon Redshift
Cassandra
Cassandra
Salesforce Sales Cloud
Salesforce Sales Cloud
Mixpanel
Mixpanel

What are some alternatives to Airflow, Matillion?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

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