Azure Data Factory vs IBM InfoSphere Information Server comparison

"The data is more scalable."

"The workflow automation features in GitLab, particularly its low code/no code approach, are highly beneficial for accelerating development speed. This feature allows for quick creation of pipelines and offers customization options for integration needs, making it versatile for various use cases. GitLab supports a wide range of connectors, catering to a majority of integration needs. Azure Data Factory's virtual enterprise and monitoring capabilities, the visual interface of GitLab makes it user-friendly and easy to teach, facilitating adoption within teams. While the monitoring capabilities are sufficient out of the box, they may not be as comprehensive as dedicated enterprise monitoring tools. GitLab's monitoring features are manageable for production use, with the option to integrate log analytics or create custom dashboards if needed. The data flow feature in Azure Data Factory within GitLab is valuable for data transformation tasks, especially for those who may not have expertise in writing complex code. It simplifies the process of data manipulation and is particularly useful for individuals unfamiliar with Spark coding. While there could be improvements for more flexibility, overall, the data flow feature effectively accomplishes its purpose within GitLab's ecosystem."

"The solution includes a feature that increases the number of processors used which makes it very powerful and adds to the scalability."

"Data Factory itself is great. It's pretty straightforward. You can easily add sources, join and lookup information, etc. The ease of use is pretty good."

"The initial setup is very quick and easy."

"Azure Data Factory's most valuable features are the packages and the data transformation that it allows us to do, which is more drag and drop, or a visual interface. So, that eases the entire process."

"It is easy to deploy workflows and schedule jobs."

"The solution has a good interface and the integration with GitHub is very useful."

"The integration with different technologies is the most valuable feature."

"This solution is extremely flexible and scalable."

"Stability-wise, I rate the solution a ten out of ten."

"IBM InfoSphere Information Server is stable."

"Snowflake connectivity was recently added and if the vendor provided some videos on how to create data then that would be helpful."

"The solution can be improved by decreasing the warmup time which currently can take up to five minutes."

"The deployment should be easier."

"One area for improvement is documentation. At present, there isn't enough documentation on how to use Azure Data Factory in certain conditions. It would be good to have documentation on the various use cases."

"Some prebuilt data source or data connection aspects are generic."

"Data Factory's monitorability could be better."

"There's no Oracle connector if you want to do transformation using data flow activity, so Azure Data Factory needs more connectors for data flow transformation."

"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."

"Their technical support needs improvement."

"IBM InfoSphere Information Server should be more scalable. It should have the option to change the configuration to run on a single, non-multiple node, or multi-threading processing."

"This solution would benefit from the engine being made more lightweight."

"There are certain shortcomings in the cloud side of the solution, where improvements are required."

"While I can't specify the actual cost, I believe it is reasonably priced and comparable to similar products."

"Data Factory is expensive."

"The pricing is a bit on the higher end."

"The price you pay is determined by how much you use it."

"The pricing model is based on usage and is not cheap."

"It seems very low initially, but as the data grows, the solution’s bills grow exponentially."

"The solution is cheap."

"This is a cost-effective solution."

"The licensing cost of IBM InfoSphere Information Server depends on how many users there are."