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.
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"It's extremely consistent."
"One of the most valuable features of Azure Data Factory is the drag-and-drop interface. This helps with workflow management because we can just drag any tables or data sources we need. Because of how easy it is to drag and drop, we can deliver things very quickly. It's more customizable through visual effect."
"The scalability of the product is impressive."
"Data Factory's best features are connectivity with different tools and focusing data ingestion using pipeline copy data."
"The most valuable aspect is the copy capability."
"It's cloud-based, allowing multiple users to easily access the solution from the office or remote locations. I like that we can set up the security protocols for IP addresses, like allow lists. It's a pretty user-friendly product as well. The interface and build environment where you create pipelines are easy to use. It's straightforward to manage the digital transformation pipelines we build."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"Offers great flexibility."
"It's a robust solution."
"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."
"The solution is stable."
"The most valuable feature is the product's versatility to inject data."
"The solution's scalability is really good...we are using multi-instance jobs where you can scale them easily."
"The ETL tools are probably the most valuable feature. It has an IBM tool, a friendly UI and it makes things more comfortable."
"DataStage works better with Linux operating systems when the application services are hosted on Linux system equipment, but it's powerful on Windows too."
"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."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"Data Factory's performance during heavy data processing isn't great."
"Data Factory has so many features that it can be a little difficult or confusing to find some settings and configurations. I'm sure there's a way to make it a little easier to navigate."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"Azure Data Factory's pricing in terms of utilization could be improved."
"Azure Data Factory could benefit from improvements in its monitoring capabilities to provide a more robust feature set. Enhancing the ease of deployment to higher environments within Azure DevOps would be beneficial, as the current process often requires extensive scripting and pipeline development. It is also known for the flexibility of the data flow feature, particularly in supporting more dynamic data-driven architectures. These enhancements would contribute to a more seamless and efficient workflow within GitLab."
"The number of standard adaptors could be extended further."
"The template mapping could be easier."
"What needs improvement in IBM InfoSphere DataStage is its pricing. The pricing for the solution is higher than its competitors, so a lot of the clients my company has worked with prefer other tools over IBM InfoSphere DataStage because of the high price tag. Another area for improvement in the solution stems from a lot of new types of databases, for example, databases in the cloud and big data have become available, and IBM InfoSphere DataStage is working on various connectors for different data sources, but that still isn't up-to-date, meaning that some connectors are missing for modern data sources. The latest version of IBM InfoSphere DataStage also has a complex architecture, so my team faced frequent outages and that should be improved as well."
"DataStage is quite expensive. It is too hard to find a consultant using DataStage in Turkey."
"The error messaging needs to be improved."
"The pricing should be lower."
"The solution can be a bit more user-friendly, similar to Informatica."
"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."
"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."
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 Microsoft Azure Synapse Analytics, 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.
