We performed a comparison between Azure Data Factory and IBM Infosphere DataStage based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison Results: Azure Data Factory is mature, robust, and consistent. The built-in connectors of more than 100 sources and onboarding data from many different sources to the cloud environment make it easier for users to better understand the data flow. Users are happier with its pricing as well. Once IBM Infosphere DataStage moves toward a focus on cloud technologies, it will become a more desirable solution in today’s cloud-focused marketplace.
"I enjoy the ease of use for the backend JSON generator, the deployment solution, and the template management."
"I can do everything I want with SSIS and Azure Data Factory."
"I like how you can create your own pipeline in your space and reuse those creations. You can collaborate with other people who want to use your code."
"What I like best about Azure Data Factory is that it allows you to create pipelines, specifically ETL pipelines. I also like that Azure Data Factory has connectors and solves most of my company's problems."
"The most valuable aspect is the copy capability."
"The trigger scheduling options are decently robust."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."
"The most valuable feature is the product's versatility to inject data."
"The most valuable feature is the data integration for data warehousing."
"The product is a stable and powerful data management solution that can run in parallel mode for enhanced speed."
"The performance optimization is quite good in DataStage. It provides parallelism and pipelining mechanisms"
"The solution's scalability is really good...we are using multi-instance jobs where you can scale them easily."
"The best feature of IBM InfoSphere DataStage for me was that it was very much user-friendly. The solution didn't require that much raw coding because most of its features were drag and drop, plus it had a large number of functionalities."
"IBM is stable and accurate to monitor. It's easy to understand to monitor the data lineage from source to target."
"As a data integration platform, it is easy to use. It is quite robust and useful for volumetric analysis when you have huge volumes of data. We have tested it for up to ten million rows, and it is robust enough to process ten million rows internally with its parallel processing. Its error logging mechanism is far simpler and easier to understand than other data integration tools. The newer version of InfoSphere has the data catalog and IDC lineage. They are helpful in the easy traceability of columns and tables."
"It does not appear to be as rich as other ETL tools. It has very limited capabilities."
"Integration of data lineage would be a nice feature in terms of DevOps integration. It would make implementation for a company much easier. I'm not sure if that's already available or not. However, that would be a great feature to add if it isn't already there."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"We are too early into the entire cycle for us to really comment on what problems we face. We're mostly using it for transformations, like ETL tasks. I think we are comfortable with the facts or the facts setting. But for other parts, it is too early to comment on."
"Lacks in-built streaming data processing."
"The need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"Some known bugs and issues with Azure Data Factory could be rectified."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"The error messaging needs to be improved."
"I'd like to be able to do more with the data and metadata, including copy and pasting, et cetera."
"The documentation and in-application help for this solution need to be improved, especially for new features."
"It doesn't have any big data connections. It would be good to have them because most of the systems are moving towards big data. There should also be a user-friendly way to interact with the cloud. Its loading process is very slow. It takes a lot of time for around 5 or 6 million records, and we are not able to provide real-time data to the vendors due to this delay. Its performance needs to be improved. It is also like a legacy system. It is not updated much. In higher versions, they only do small changes. We would like to have new features and new technologies."
"The setup is extremely difficult."
"In the future, I would like to see more integration with cloud technologies."
"The initial setup could be more straightforward."
"Its documentation is not up to the mark. While building APIs, we had a lot of problems trying to get around it because it is not very user-friendly. We tried to get hold of API documentation, but the documentation is not very well thought out. It should be more structured and elaborate. In terms of additional features, I would like to see good reporting on performance and performance-tuning recommendations that can be based on AI. I would also like to see better data profiling information being reported on InfoSphere."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while IBM InfoSphere DataStage is ranked 7th in Data Integration with 37 reviews. Azure Data Factory is rated 8.0, while IBM InfoSphere DataStage is rated 7.8. The top reviewer of Azure Data Factory writes "The data factory agent is quite good but pricing needs to be more transparent". On the other hand, the top reviewer of IBM InfoSphere DataStage writes "User-friendly with a lot of functions for transmission rules, but has slow performance and not suitable for a huge volume of data". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and Palantir Foundry, whereas IBM InfoSphere DataStage is most compared with SSIS, IBM Cloud Pak for Data, Talend Open Studio, Informatica PowerCenter and IBM InfoSphere Information Server. See our Azure Data Factory vs. IBM InfoSphere DataStage report.
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