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 Tools 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 most valuable feature is the copy activity."
"When it comes to our business requirements, this solution has worked well for us. However, we have not stretched it to the limit."
"Microsoft supported us when we planned to provision Azure Data Factory over a private link. As a result, we received excellent support from Microsoft."
"The most valuable features of Azure Data Factory are the flexibility, ability to move data at scale, and the integrations with different Azure components."
"An excellent tool for pipeline orchestration."
"The most valuable feature of Azure Data Factory is that it has a good combination of flexibility, fine-tuning, automation, and good monitoring."
"This solution has provided us with an easier, and more efficient way to carry out data migration tasks."
"It is really easy to set up and the interface is easy to use."
"In StreamSets, everything is in one place."
"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."
"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."
"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."
"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."
"Data Factory's cost is too high."
"There is no built-in pipeline exit activity when encountering an error."
"There is no built-in function for automatically adding notifications concerning the progress or outline of a pipeline run."
"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."
"It does not appear to be as rich as other ETL tools. It has very limited capabilities."
"They require more detailed error reporting, data normalization tools, easier connectivity to other services, more data services, and greater compatibility with other commonly used schemas."
"There is always room to improve. There should be good examples of use that, of course, customers aren't always willing to share. It is Catch-22. It would help the user base if everybody had really good examples of deployments that worked, but when you ask people to put out their good deployments, which also includes me, you usually got, "No, I'm not going to do that." They don't have enough good examples. Microsoft probably just needs to pay one of their partners to build 20 or 30 examples of functional Data Factories and then share them as a user base."
"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 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."
"We've seen a couple of cases where it appears to have a memory leak or a similar problem."
"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."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"Sometimes, when we have large amounts of data that is very efficiently stored in Hadoop or Kafka, it is not very efficient to run it through StreamSets, due to the lack of efficiency or the resources that StreamSets is using."
"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."
Create, schedule, and manage your data integration at scale with Azure Data Factory - a hybrid data integration (ETL) service. Work with data wherever it lives, in the cloud or on-premises, with enterprise-grade security.
StreamSets offers an end-to-end data integration platform to build, run, monitor and manage smart data pipelines that deliver continuous data for DataOps, and power the modern data ecosystem and hybrid integration.
Only StreamSets provides a single design experience for all design patterns for 10x greater developer productivity; smart data pipelines that are resilient to change for 80% less breakages; and a single pane of glass for managing and monitoring all pipelines across hybrid and cloud architectures to eliminate blind spots and control gaps.
With StreamSets, you can deliver the continuous data that drives the connected enterprise.
Azure Data Factory is ranked 2nd in Data Integration Tools with 35 reviews while StreamSets is ranked 11th in Data Integration Tools with 6 reviews. Azure Data Factory is rated 8.0, while StreamSets is rated 8.4. The top reviewer of Azure Data Factory writes "There's the good, the bad and the ugly....unfortunately lots of ugly". On the other hand, the top reviewer of StreamSets writes "Integrates with different enterprise systems and enables us to easily build data pipelines without knowing how to code". Azure Data Factory is most compared with Informatica PowerCenter, Informatica Cloud Data Integration, Alteryx Designer, Microsoft Azure Synapse Analytics and Talend Open Studio, whereas StreamSets is most compared with Informatica PowerCenter, SSIS, Spring Cloud Data Flow, Oracle GoldenGate and Talend Open Studio. See our Azure Data Factory vs. StreamSets report.
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