We performed a comparison between Mule Anypoint Platform and StreamSets based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The community manager is the standout feature of the Mule Anypoint Platform, as it provides a platform to showcase your API to external parties. It is exceptionally user-friendly and enables external parties to easily utilize and consume the APIs."
"Mule works very well with Salesforce and CRMs."
"The API toolkit is the solution's most valuable aspect at this time, for our organization."
"The most valuable feature is the variety of characters Anypoint Platform has. It is very scalable and customizable."
"The most valuable feature of the solution is a huge list of available connectors for a lot of different platforms, which we can use very easily."
"The solution's deployment and proxy processes are very good."
"This is the easiest and best tool available."
"The initial setup is quite easy because the solution has a good interface through which the configuration, mapping, and so on can be done."
"It is really easy to set up and the interface is easy to use."
"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."
"In StreamSets, everything is in one place."
"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."
"The best feature that I really like is the integration."
"What I love the most is that StreamSets is very light. It's a containerized application. It's easy to use with Docker. If you are a large organization, it's very easy to use Kubernetes."
"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 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."
"I would like to see some data integration and automation."
"Lacks intelligent management data and intelligent mappings."
"Better documentation, in particular with respect to the initial setup, would be helpful."
"In order to set up a storefront, we currently rely on a third-party solution. It would greatly enhance our operations if this feature was integrated into their existing solution."
"The high price of the product is an area of concern where improvements are required."
"The compatibility with vendors can be improved. Microsoft Azure heavily uses single software."
"Pricing is one aspect of the solution that is troublesome. It's too expensive for smaller organizations."
"We would like an entire DevOps in place in this particular solution."
"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."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"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."
"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."
"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."
"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."
"One area for improvement could be the cloud storage server speed, as we have faced some latency issues here and there."
Mule Anypoint Platform is ranked 3rd in Cloud Data Integration with 41 reviews while StreamSets is ranked 8th in Data Integration with 24 reviews. Mule Anypoint Platform is rated 8.2, while StreamSets is rated 8.4. The top reviewer of Mule Anypoint Platform writes "Robust, reliable, and stable, ensuring high availability for critical integrations". 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". Mule Anypoint Platform is most compared with MuleSoft Composer, Microsoft Azure Logic Apps, SAP Process Orchestration, Oracle Integration Cloud Service and SAP Cloud Platform, whereas StreamSets is most compared with Fivetran, Informatica PowerCenter, Azure Data Factory, SSIS and webMethods Integration Server. See our Mule Anypoint Platform vs. StreamSets report.
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