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.
"We have found the bulk load feature very valuable."
"The most valuable feature is the copy activity."
"I can do everything I want with SSIS and Azure Data Factory."
"The scalability of the product is impressive."
"Data Factory's best features include its data source connections, GUI for building data pipelines, and target loading within Azure."
"The tool's most valuable features are its connectors. It has many out-of-the-box connectors. We use ADF for ETL processes. Our main use case involves integrating data from various databases, processing it, and loading it into the target database. ADF plays a crucial role in orchestrating these ETL workflows."
"Data Factory's best feature is the ease of setting up pipelines for data and cloud integrations."
"Data Flow and Databricks are going to be extremely valuable services, allowing data solutions to scale as the business grows and new data sources are added."
"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 product is easy to deploy."
"The solution is stable."
"When we have needed help from the IBM team, they were helpful. Our company is a premium partner so we get fast responses."
"DataStage works better with Linux operating systems when the application services are hosted on Linux system equipment, but it's powerful on Windows too."
"The product is a stable and powerful data management solution that can run in parallel mode for enhanced speed."
"In IBM DataStage, the Transformer is the most valuable feature for me. It enables me to apply complex transformations, generate the gateway key, and map source tables into the session table."
"The solution's scalability is really good...we are using multi-instance jobs where you can scale them easily."
"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."
"The solution should offer better integration with Azure machine learning. We should be able to embed the cognitive services from Microsoft, for example as a web API. It should allow us to embed Azure machine learning in a more user-friendly way."
"It can improve from the perspective of active logging. It can provide active logging information."
"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."
"I have not found any real shortcomings within the product."
"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."
"The one element of the solution that we have used and could be improved is the user interface."
"My only problem is the seamless connectivity with various other databases, for example, SAP."
"It would be useful to provide support for Python, AR, and Java."
"The pricing should be lower."
"The template mapping could be easier."
"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."
"The interface needs improvement. It is really too technical. That is the main problem."
"The initial setup could be more straightforward."
"The graphical user interface (GUI) feels a lot like the interfaces from the 1980s."
"There could be more customization options for the product."
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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