Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL. Created at Airbnb as an open-source project in 2014, Airflow was brought into the Apache Software Foundation’s Incubator Program 2016 and announced as Top-Level Apache Project in 2019. It is used as a data orchestration solution, with over 140 integrations and community support.
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Zapier
Score 9.0 out of 10
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The Zapier Automation Platform designed to integrate data between web apps. It is scaled for small to mid-sized businesses, with a functional but limited free version of the program.
For a quick job scanning of status and deep-diving into job issues, details, and flows, AirFlow does a good job. No fuss, no muss. The low learning curve as the UI is very straightforward, and navigating it will be familiar after spending some time using it. Our requirements are pretty simple. Job scheduler, workflows, and monitoring. The jobs we run are >100, but still is a lot to review and troubleshoot when jobs don't run. So when managing large jobs, AirFlow dated UI can be a bit of a drawback.
If you have processes that are now managed and controlled using a spreadsheet, Zapier will give you a lot more control over what is happening and will help you increase productivity by eliminating simple steps such as sending emails and sharing information with your colleagues. It frees time for very transactional activities.
Ease of use - multiple people in the organization can set up and run Zaps per their specific use cases without much training.
Connectivity - Zapier is able to connect to multiple applications we use on a regular basis.
Functionality - Zapier provides embedded functionality within the app itself (email, data conversion), but also appropriate triggers and actions for apps it connects to.
Versatile - Zapier can execute complicated and simple tasks and thus has many use cases.
The interface is very user-friendly, and there are also many tools to help a brand-new user get started. For example, you can put your Zap idea into the AI bot, and it will basically build a shell of your Zap to get started on. The format for each step within a Zap is also very helpful (set up the connection/app, set up the fields/details, then test).
Before we purchased Zapier, I contacted support and asked them if Zapier could support my intended workflow (this is actually a selection on their support form - awesome). Within 2 hours, I was contacted by a support team member who seemed sure it would work, but granted me premium access for 2 weeks to try it out for myself. Sure enough, it did! Ever since then, support has replied rapidly to any problems I have experienced and answered my questions within a few sentences.
There are a number of reasons to choose Apache Airflow over other similar platforms- Integrations—ready-to-use operators allow you to integrate Airflow with cloud platforms (Google, AWS, Azure, etc) Apache Airflow helps with backups and other DevOps tasks, such as submitting a Spark job and storing the resulting data on a Hadoop cluster It has machine learning model training, such as triggering a Sage maker job.
We actually utilize both Integromat and Zapier at our company, for all the reasons detailed in this review. Though Zapier is excellent for simple client integrations, we often run into internal use cases that require complexity that Zapier cannot provide. Specifically working with API calls (not just webhooks), complex multi-step integrations with Routing/parsing/etc, and large volume integrations. Integromat is perfect for these use cases, but doesn’t provide the simplicity and account scalability that Zapier offers.