Apache Airflow vs. Make

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.

Incentivized

Integrating your CRM with Marketing Applications for data transmission and unity, GDPR compliance, syncing. Build a scenario for each specific (language or location) action. Managing certain actions and triggers based on links, some of the workflow solutions were not present in marketing tools and we need to create more complex process in Make to meet our needs. Lead and contact tracking from Social Media, updating our inventory based on user actions.

• Apache Airflow is one of the best Orchestration platforms and a go-to scheduler for teams building a data platform or pipelines.
• Apache Airflow supports multiple operators, such as the Databricks, Spark, and Python operators. All of these provide us with functionality to implement any business logic.
• Apache Airflow is highly scalable, and we can run a large number of DAGs with ease. It provided HA and replication for workers. Maintaining airflow deployments is very easy, even for smaller teams, and we also get lots of metrics for observability.

Incentivized

• The ability to integrate with any API
• The drag and the drop builder makes the process of automating tasks easier
• Rich library of the most used apps

Incentivized

• A local "dry run" or IDE plugin that can validate and simulate DAG execution without needing a full environment.
• Better feedback on DAG parse errors in the UI or CLI.
• Navigating large DAGs with hundreds of tasks can be slow and hard to understand visually.

Incentivized

• Better navigational capabilities from scenarios
• Better use of AI or prompting for complex things like iterators/aggregators
• A "test mode" so that you don't have a ton of runs that are invalid or to be able to populate dummy data without wasting unnecessary operations to create it.

Incentivized

At this point, it is firmly embedded in the DNA of the business and to give up the ability to automate workflows and create integrations on the fly would be a terrible idea.

For its capability to connect with multicloud environments. Access Control management is something that we don't get in all the schedulers and orchestrators. But although it provides so many flexibility and options to due to python , some level of knowledge of python is needed to be able to build workflows.

Incentivized

I think it is the easiest workflow tool that I have ever used. Drag and drop works perfectly, helping less computer friendly users to simplify and nest their workflows. Managers without IT experience are now dealing separately with most of issues on their own. Handover of tasks and workflows is also easier as it is possible to comment and explain everything inside one.

The pricing schema is very attractive, almost 50% lower than the competition. You could start from free and then grow. It has a pretty big library of connections to other apps and services, which really helps you when everything is a mess. Integromat has a really easy-to-use interface. You could do almost everything with fewer than 5 clicks. Scenarios (automation steps to complete a routine) have graphics so you can configure them more easily.

Incentivized

Apache Airflow is suited for a much wider set of use cases compared to Databricks. You can run it anywhere, and there is also no vendor lock-in. With Airflow, we can utilize almost any compute engine. Same thing we want to do with Databricks. There might be some level of difficulty based on the support.

Incentivized

Integromat allows us to do everything we used to do on Zapier but it doesn't limit us to only the popular apps, with Integromat we're integrating custom APIs and we get data from different servers through GET requests and it's exactly what we needed and Zapier couldn't provide it.

Incentivized

• Most of the ETL processes were automated, cutting down on human labor.
• Apache Airflow's user interface (UI) was very informative and straightforward.
• Since ETL processes were providing data via airflow, we were able to gain a deeper comprehension of the data at hand.

Incentivized

• Enabled us to solve use cases for hundreds of clients and hundreds of different platforms
• Provided the customization capabilities to automate accounting/invoicing processes that save dozens of man-hours a month
• Allowed us to build custom churn/retention/engagement scores that have driven a 20% reduction in churn

Incentivized