Apache Airflow is an open source tool that can be used to programmatically author, schedule and monitor data pipelines using Python and SQL.
N/A
JAMS
Score 8.3 out of 10
N/A
JAMS is a centralized workload automation and job scheduling solution that runs, monitors, and manages jobs and workflows. Reliably orchestrate the critical IT processes that run your business from a single pane of glass.
$9,996
per year
Pricing
Apache Airflow
JAMS
Editions & Modules
No answers on this topic
Core
9,996.00
per year
Advanced
Customized Pricing
per year
Offerings
Pricing Offerings
Apache Airflow
JAMS
Free Trial
No
Yes
Free/Freemium Version
Yes
No
Premium Consulting/Integration Services
No
Yes
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
—
- Core: For small teams getting started with automation. Core Integrations: PowerShell, SQL, Azure Data Factory, Python, 20+ others.
- Advanced: Comprehensive solution for large-scale operations. Core Integrations: SAP, JDE, Ellucian Banner, Informatica, Mainframe and Power Systems.
Airflow is well-suited for data engineering pipelines, creating scheduled workflows, and working with various data sources. You can implement almost any kind of DAG for any use case using the different operators or enforce your operator using the Python operator with ease. The MLOps feature of Airflow can be enhanced to match MLFlow-like features, making Airflow the go-to solution for all workloads, from data science to data engineering.
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.
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.
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.
UI/Dashboard can be updated to be customisable, and jobs summary in groups of errors/failures/success, instead of each job, so that a summary of errors can be used as a starting point for reviewing them.
Navigation - It's a bit dated. Could do with more modern web navigation UX. i.e. sidebars navigation instead of browser back/forward.
Again core functional reorg in terms of UX. Navigation can be improved for core functions as well, instead of discovery.
JAMS is a critical resource free up people to do other things and ensuring that processes and tasks are run consistently. We are also confident that procedures are run consistently and on time or as soon as the necessary data is available. With automated job failure notification, we are not required to check that jobs are running correctly.
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.
JAMS is very user friendly; you hardly need to do coding. The only thing that I would say a challenge is setting it up, but that's because you barely know the product yet and, in every processing, setting up is the difficult part. But once you've set it up and you are going to use it, you will really feel that it is worth to invest in this kind of software solution, it really does it job very well.
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
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
I've never had to wait more than a day for a response to any email queries submitted. We had a very positive experience using support hours during out migration process from v6 to v7. We've also recently had a weeklong group training course where all attendees were positive about the learning outcomes, a shoutout to Jose who did both the migration and the weeklong course!
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.
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
Multiple DAGs can be orchestrated simultaneously at varying times, and runs can be reproduced or replicated with relative ease. Overall, utilizing Apache Airflow is easier to use than other solutions now on the market. It is simple to integrate in Apache Airflow, and the workflow can be monitored and scheduling can be done quickly using Apache Airflow. We advocate using this tool for automating the data pipeline or process.
I had evaluated 2 others in 2010/2011, but I do not remember their names. This was the easier one to work with and had a better looking, sometimes more professional looking UI than what I was evaluating. JAMS was more scaleable and had the ability to make custom interfaces to more systems through Execution Methods that could be tailored.
The product is quite flexible. There are a number of features and functions that we use on a daily basis, and there are many features that are available that we have not yet needed or explored (like setting up jobs with the ability to do FTP or Sftp file transfers).
Impact Depends on number of workflows. If there are lot of workflows then it has a better usecase as the implementation is justified as it needs resources , dedicated VMs, Database that has a cost
Using JAMS when working from home (initially COVID, and now permanent) gives me tremendous visibility into the running operations of our business without any loss in productivity for not being in the office.
With JAMS I can more tightly schedule evening batch jobs by running one job after the successful completion of predecessor, as opposed to the CRON like guessing at safe start times.
Central control on a monitored server in a datacenter for all job scheduling tasks has given us 99.9% uptime reliability, instead of herding cats on multiple machines.
