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."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."
"The flexibility that Azure Data Factory offers is great."
"It is beneficial that the solution is written with Spark as the back end."
"The most valuable features are data transformations."
"Data Factory's most valuable feature is Copy Activity."
"We use the solution to move data from on-premises to the cloud."
"The overall performance is quite good."
"The two most valuable features of Azure Data Factory are that it's very scalable and that it's also highly reliable."
"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 ability to have a good bifurcation rate and fewer mistakes is valuable."
"The entire user interface is very simple and the simplicity of creating pipelines is something that I like very much about it. The design experience is very smooth."
"I have used Data Collector, Transformer, and Control Hub products from StreamSets. What I really like about these products is that they're very user-friendly. People who are not from a technological or core development background find it easy to get started and build data pipelines and connect to the databases. They would be comfortable like any technical person within a couple of weeks."
"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."
"For me, the most valuable features in StreamSets have to be the Data Collector and Control Hub, but especially the Data Collector. That feature is very elegant and seamlessly works with numerous source systems."
"The UI is user-friendly, it doesn't require any technical know-how and we can navigate to social media or use it more easily."
"The scheduling within the data engineering pipeline is very much appreciated, and it has a wide range of connectors for connecting to any data sources like SQL Server, AWS, Azure, etc. We have used it with Kafka, Hadoop, and Azure Data Factory Datasets. Connecting to these systems with StreamSets is very easy."
"It's a good idea to take a Microsoft course. Because they are really helpful when you start from your journey with Data Factory."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"I have not found any real shortcomings within the product."
"Azure Data Factory should be cheaper to move data to a data center abroad for calamities in case of disasters."
"There should be a way that it can do switches, so if at any point in time I want to do some hybrid mode of making any data collections or ingestions, I can just click on a button."
"We have experienced some issues with the integration. This is an area that needs improvement."
"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 need to work more on developing out-of-the-box connectors for other products like Oracle, AWS, and others."
"The software is very good overall. Areas for improvement are the error logging and the version history. I would like to see better, more detailed error logging information."
"Sometimes, it is not clear at first how to set up nodes. A site with an explanation of how each node works would be very helpful."
"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."
"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."
"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."
"The monitoring visualization is not that user-friendly. It should include other features to visualize things, like how many records were streamed from a source to a destination on a particular date."
"Using ETL pipelines is a bit complicated and requires some technical aid."
"I would like to see it integrate with other kinds of platforms, other than Java. We're going to have a lot of applications using .NET and other languages or frameworks. StreamSets is very helpful for the old Java platform but it's hard to integrate with the other platforms and frameworks."
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 webMethods.io Integration. See our Azure Data Factory vs. StreamSets report.
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