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 most valuable aspect is the copy capability."
"I can do everything I want with SSIS and Azure Data Factory."
"The data mapping and the ability to systematically derive data are nice features. It worked really well for the solution we had. It is visual, and it did the transformation as we wanted."
"It is very modular. It works well. We've used Data Factory and then made calls to libraries outside of Data Factory to do things that it wasn't optimized to do, and it worked really well. It is obviously proprietary in regards to Microsoft created it, but it is pretty easy and direct to bring in outside capabilities into Data Factory."
"Allows more data between on-premises and cloud solutions"
"For developers that are very accustomed to the Microsoft development studio, it's very easy for them to complete end-to-end data integration."
"Data Factory's best features are simplicity and flexibility."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"In StreamSets, everything is in one place."
"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."
"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."
"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."
"It's very easy to integrate. It integrates with Snowflake, AWS, Google Cloud, and Azure. It's very helpful for DevOps, DataOps, and data engineering because it provides a comprehensive solution, and it's not complicated."
"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."
"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."
"Lacks in-built streaming data processing."
"We have experienced some issues with the integration. This is an area that needs improvement."
"I would like to see this time travel feature in Snowflake added to Azure Data Factory."
"The product could provide more ways to import and export data."
"I rate Azure Data Factory six out of 10 for stability. ADF is stable now, but we had problems recently with indexing on an SQL database. It's slow when dealing with a huge volume of data. It depends on whether the database is configured as general purpose or hyperscale."
"User-friendliness and user effectiveness are unquestionably important, and it may be a good option here to improve the user experience. However, I believe that more and more sophisticated monitoring would be beneficial."
"A room for improvement in Azure Data Factory is its speed. Parallelization also needs improvement."
"The pricing scheme is very complex and difficult to understand."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"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."
"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."
"StreamSet works great for batch processing but we are looking for something that is more real-time. We need latency in numbers below milliseconds."
"The data collector in StreamSets has to be designed properly. For example, a simple database configuration with MySQL DB requires the MySQL Connector to be installed."
"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."
"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."
"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."
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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