We performed a comparison between SnapLogic and Spring Cloud Data Flow 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 connection with SOAP is the best feature."
"By using snaps instead of functions in code, you can see the building blocks of the integration visually. This helps a lot."
"SnapLogic is more user-friendly than Boomi in terms of debugging. You can move the mouse to a place, and it will record and show the data easily."
"Despite having no prior experience in SnapLogic, we managed to build, test, and prepare it for release in just three hours, handling heavy data efficiently."
"The feature I found most valuable in SnapLogic is low-code development. Low-code development has been very useful for simple processes, which is required for business users such as extracting details from a file or getting things reported by calling your web service. Calling your web service also becomes easier with SnapLogic because of the snaps available, so if you have the documentation, you can call an API. You don't have to write all those clients to call an API, so that is another feature I found very easy in SnapLogic. Configuring and managing all the file systems also become very handy with the solution."
"You can use other languages, such as Python, and easily connect to other systems."
"The solution is easy to implement and easy to use. It's basically just drag and drop."
"It's more developer-friendly, and development can be done at a faster phase."
"There are a lot of options in Spring Cloud. It's flexible in terms of how we can use it. It's a full infrastructure."
"The most valuable feature is real-time streaming."
"The most valuable features of Spring Cloud Data Flow are the simple programming model, integration, dependency Injection, and ability to do any injection. Additionally, auto-configuration is another important feature because we don't have to configure the database and or set up the boilerplate in the database in every project. The composability is good, we can create small workloads and compose them in any way we like."
"The product is very user-friendly."
"The dashboards regarding scheduled tasks need further improvement."
"What could be improved in SnapLogic is that it was not capable in terms of processing a large number of datasets, but at that point, SnapLogic was evolving. It didn't give a lot of Snaps. I heard recently there are a lot of Snaps getting added and the solution was being enhanced, particularly to connect different data sources. When I was working with SnapLogic six months to one year back, I faced the issue of it not being capable of handling a huge volume of datasets or didn't have much of Snaps, and that was the drawback. If there is any large number of data sets, that's based on or depends on your configuration. If it is a huge volume of data, other traditional ETL tools such as Informatica and Talend can process millions and billions of records, while in SnapLogic, the Snaplex fails or it returns an error in terms of processing that huge volume of data. Informatica, Talend, or any other ETL tool can run for hours in terms of jobs, while SnapLogic jobs fail when the threshold is reached. SnapLogic isn't able to withstand processing, but I don't know if that's still an issue at present, because the solution is getting enhanced and it's been more than six months to one year since I last worked with SnapLogic. There are now a lot of Snaps getting added to the solution, and if it can overcome the limitations I mentioned, SnapLogic could be the go-to tool because currently, it's not being used as much in organizations. It's being used comparatively less compared to other retail tools."
"They should expand in terms of features for SaaS-based market requirements in different sectors."
"SnapLogic sits somewhere in the middle. It doesn’t offer enough easy canned integrations for its users like some of the easier to use integration apps."
"I would like to see more performance-related dashboards, ones that display the cost of a pipeline, for instance. Also, it would be helpful to have management dashboards for overseeing pipelines and connections."
"The support is the most important improvement they could make."
"One area for improvement in SnapLogic is the transparency in the flow of data. It needs to have more transparency. Right now, users only have a preview option at the end of any job flow, so at the end of any Snap Pack, there is a data preview option that lets you review the data and see how it's moving. What would make the solution better is more debugging and more access to change data from the preview panel or more functionality in terms of the preview option."
"The problem is that SnapLogic doesn't offer a wide variety of connectors. For example, integrating with Salesforce is not that easy."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"Spring Cloud Data Flow could improve the user interface. We can drag and drop in the application for the configuration and settings, and deploy it right from the UI, without having to run a CI/CD pipeline. However, that does not work with Kubernetes, it only works when we are working with jars as the Spring Cloud Data Flow applications."
SnapLogic is ranked 14th in Data Integration with 21 reviews while Spring Cloud Data Flow is ranked 28th in Data Integration with 5 reviews. SnapLogic is rated 8.0, while Spring Cloud Data Flow is rated 8.0. The top reviewer of SnapLogic writes "Easy to set up, easy to use, and is low-code". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". SnapLogic is most compared with AWS Glue, IBM InfoSphere DataStage, Azure Data Factory, Informatica Cloud Data Integration and SSIS, whereas Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and TIBCO BusinessWorks. See our SnapLogic vs. Spring Cloud Data Flow report.
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