Apache Airflow vs. JAMS

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

It's currently one of the best of the lower entry cost options out there, as it currently is a set license cost, not based on the number of jobs executed. In the hands of a good script writer and users with workflow experience, it's a powerful tool to accomplish just about any process that you have a need to complete.

Incentivized

• 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 Activity Monitor clearly shows the Running Jobs, and Jobs that are to run soon. Successful Jobs can be viewed as well. The Refresh of this monitor is completely customizable to your liking.
• Job Definitions are very well organized by use of Folders. This simplifies the structure of how to best Implement JAMS Jobs, including the ability to provide specific properties on each folder - whereby Jobs will inherit these properties.
• Connectivity to servers is well thought out by having Shortcuts to include Credentials and Connection Store for server information.
• JAMS Jobs can be controlled via System Resources. This is very powerful and is a very useful configuration found in JAMS.

• 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

• It would be very helpful if the application had the ability to display help text based on where the cursor is hovering on the screen. There are many times when a brief explanation of an on-screen prompt would be very handy. For example, when you attempt to Cancel a job from the Monitor, you are presented with the checkbox that says "Reprocess completion?" It would be very nice if you could hover over the prompt and see a pop-up help screen that explains what happens if you check this checkbox. The same applies to all the checkbox options presented when you attempt to "Release" a job from the Monitor.

Incentivized

We have built JAMS into our scheduling process. Its a great scheduling tool. I'm not 100% on the execution side as we have had issues with what i'll refer to as compatibility issues with ssis variables, but it executes sql agent jobs perfectly, so when i have an issue i create a sql agent job and have JAMS execute the sql agent job on the schedule from JAMS

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

9/10 as there are so many features I have not tried as of yet. It is easy to get started but as jobs become more complex you tend to employ more and more features - Some of which can be complicated at first. This all comes down to experience using the system. Out first setup and current setup are vastly different as we learn how to use the full power.

We didnt really encounter any downtime, no issues encountered during 2 years of use of JAMs also our client barely raise an issue with JAMS, mostly the issues is on the batch jobs that jams executes. So I would gave it a perfect 10, very reliable hardly encounters any error and bug

Incentivized

JAMS performance is very great, there are no issues raised with the performance, it just like nothing happens on the job after integration it gives you this monitoring capability, no reports and bugs raised on the performance, we didnt do integration with other software only database and with use of JAMS agent to different servers

Incentivized

I am giving support an 11, the turn around time is insane. At times I get a reply in minutes. The directions to fix are precise and easy to follow. They are personable and friendly and never treat me less than they would a fortune 500 company (which I am not one of).

Incentivized

People that were involved in the POC found the training a lot easier to follow. I think most people would have preferred to just get the training material and run through themselves.

Incentivized

I Was not part of the original Implementation, and the persons did that are no longer with the Organization. But I was part of the recent Upgrade process a year ago and I am the JAMS admin and was very pleased

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

We chose JAMS because it was the best solution for our business needs, a major factor being the licensing model and the simplicity of the app. We needed to migrate things fast too and this was simply plug and play without too much headache

Incentivized

By installing Agents on servers throughout the organizations, you can run a DB Script on a DB Server, move files around the network and kick off jobs on servers in different parts of the organization: JAMS Runs on its own server, isolated from others. Through an agent on a Processing Server (work Server) files can be picked up, processed and moved to a destination server to be processed into another application on another server. You don't need an agent if you are just moving files around, you need an agent if you want to run a process or API Call on the destination server.

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

• SOX auditing has been part automated saving days of work for the people involved.
• The ability for jobs to react to different failure values has enabled us to do away with overnight human monitoring ultimately contributing to saving us in the 6 figures.

Incentivized