We performed a comparison between Azure Data Factory and StreamSets based on real PeerSpot user reviews.
Find out in this report how the two Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The solution can scale very easily."
"The initial setup is very quick and easy."
"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."
"We use the solution to move data from on-premises to the cloud."
"Allows more data between on-premises and cloud solutions"
"One advantage of Azure Data Factory is that it's fast, unlike SSIS and other on-premise tools. It's also very convenient because it has multiple connectors. The availability of native connectors allows you to connect to several resources to analyze data streams."
"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."
"We haven't had any issues connecting it to other products."
"StreamSets Transformer is a good feature because it helps you when you are developing applications and when you don't want to write a lot of code. That is the best feature overall."
"StreamSets data drift feature gives us an alert upfront so we know that the data can be ingested. Whatever the schema or data type changes, it lands automatically into the data lake without any intervention from us, but then that information is crucial to fix for downstream pipelines, which process the data into models, like Tableau and Power BI models. This is actually very useful for us. We are already seeing benefits. Our pipelines used to break when there were data drift changes, then we needed to spend about a week fixing it. Right now, we are saving one to two weeks. Though, it depends on the complexity of the pipeline, we are definitely seeing a lot of time being saved."
"The best feature that I really like is the integration."
"The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customize it to do what you need. Many other tools have started to use features similar to those introduced by StreamSets, like automated workflows that are easy to set up."
"It is a very powerful, modern data analytics solution, in which you can integrate a large volume of data from different sources. It integrates all of the data and you can design, create, and monitor pipelines according to your requirements. It is an all-in-one day data ops solution."
"The most valuable feature is the pipelines because they enable us to pull in and push out data from different sources and to manipulate and clean things up within them."
"One of the things I like is the data pipelines. They have a very good design. Implementing pipelines is very straightforward. It doesn't require any technical skill."
"In StreamSets, everything is in one place."
"You cannot use a custom data delimiter, which means that you have problems receiving data in certain formats."
"Sometimes I need to do some coding, and I'd like to avoid that. I'd like no-code integrations."
"The thing we missed most was data update, but this is now available as of two weeks ago."
"DataStage is easier to learn than Data Factory because it's more visual. Data Factory has some drag-and-drop options, but it's not as intuitive as DataStage. It would be better if they added more drag-and-drop features. You can start using DataStage without knowing the code. You don't need to learn how the code works before using the solution."
"Azure Data Factory uses many resources and has issues with parallel workflows."
"Real-time replication is required, and this is not a simple task."
"The one element of the solution that we have used and could be improved is the user interface."
"The product's technical support has certain shortcomings, making it an area where improvements are required."
"I would like to see further improvement in the UI. In addition, upgrades are not automatic and they should be automated. Currently, we have to manually upgrade versions."
"The design experience is the bane of our existence because their documentation is not the best. Even when they update their software, they don't publish the best information on how to update and change your pipeline configuration to make it conform to current best practices. We don't pay for the added support. We use the "freeware version." The user community, as well as the documentation they provide for the standard user, are difficult, at best."
"There aren't enough hands-on labs, and debugging is also an issue because it takes a lot of time. Logs are not that clear when you are debugging, and you can only select a single source for a pipeline."
"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."
"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."
"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."
"In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."
"They need to improve their customer care services. Sometimes it has taken more than 48 hours to resolve an issue. That should be reduced. They are aware of small or generic issues, but not the more technical or deep issues. For those, they require some time, generally 48 to 72 hours to respond. That should be improved."
Azure Data Factory is ranked 1st in Data Integration with 81 reviews while StreamSets is ranked 8th in Data Integration with 24 reviews. Azure Data Factory is rated 8.0, while StreamSets is rated 8.4. 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 StreamSets writes "We no longer need to hire highly skilled data engineers to create and monitor data pipelines". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Snowflake and IBM InfoSphere DataStage, whereas StreamSets is most compared with Fivetran, Informatica PowerCenter, SSIS, IBM InfoSphere DataStage and Oracle GoldenGate. See our Azure Data Factory vs. StreamSets report.
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