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.
"The function of the solution is great."
"The solution is okay."
"I think it makes it very easy to understand what data flow is and so on. You can leverage the user interface to do the different data flows, and it's great. I like it a lot."
"I like that it's a monolithic data platform. This is why we propose these solutions."
"I am one hundred percent happy with the stability."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"The feature I found most helpful in Azure Data Factory is the pipeline feature, including being able to connect to different sources. Azure Data Factory also has built-in security, which is another valuable feature."
"The most valuable aspect is the copy capability."
"The performance optimization is quite good in DataStage. It provides parallelism and pipelining mechanisms"
"Highly customizable: Allowing you to handle multiple data latencies (scheduled batch, on-demand, and real-time) in the same job."
"The product is easy to deploy."
"The solution is stable."
"The most valuable feature is the ability to transfer information via notes."
"It works with multiple servers and offers high availability."
"The solution's scalability is really good...we are using multi-instance jobs where you can scale them easily."
"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."
"Lacks in-built streaming data processing."
"There are limitations when processing more than one GD file."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"Data Factory would be improved if it were a little more configuration-oriented and not so code-oriented and if it had more automated features."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"Areas for improvement in Azure Data Factory include connectivity and integration. When you use integration runtime, whenever there's a failure, the backup process in Azure Data Factory takes time, so this is another area for improvement."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"There aren't many third-party extensions or plugins available in the solution."
"The interface needs improvement."
"In the future, I would like to see more integration with cloud technologies."
"It would be great if they can include some basic version of data quality checking features."
"In terms of intermediate storage, we have some challenges, especially with customers who store data in intermediate locations."
"The solution should be more user-friendly."
"The initial setup can be complex."
"We would be happy to see in next versions the ability to return several parameters from jobs. Now, jobs can return just one parameter. If they could return several parameters, that would be great."
"The solution can be a bit more user-friendly, similar to Informatica."
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 IBM Cloud Pak for Data, SSIS, Talend Open Studio, Informatica PowerCenter and IBM InfoSphere Information Server. See our Azure Data Factory vs. IBM InfoSphere DataStage report.
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We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
