We performed a comparison between Spring Cloud Data Flow and StreamSets based on real PeerSpot user reviews.
Find out what your peers are saying about Microsoft, Informatica, Oracle and others in Data Integration."The most valuable feature is real-time streaming."
"The product is very user-friendly."
"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."
"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."
"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 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."
"It is really easy to set up and the interface is easy to use."
"The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customize it to do what you need. Many other tools have started to use features similar to those introduced by StreamSets, like automated workflows that are easy to set up."
"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 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."
"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."
"The most valuable would be the GUI platform that I saw. I first saw it at a special session that StreamSets provided towards the end of the summer. I saw the way you set it up and how you have different processes going on with your data. The design experience seemed to be pretty straightforward to me in terms of how you drag and drop these nodes and connect them with arrows."
"On the tool's online discussion forums, you may get stuck with an issue, making it an area where improvements are required."
"Some of the features, like the monitoring tools, are not very mature and are still evolving."
"The configurations could be better. Some configurations are a little bit time-consuming in terms of trying to understand using the Spring Cloud documentation."
"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."
"The documentation is inadequate and has room for improvement because the technical support does not regularly update their documentation or the knowledge base."
"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."
"StreamSet works great for batch processing but we are looking for something that is more real-time. We need latency in numbers below milliseconds."
"Visualization and monitoring need to be improved and refined."
"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."
"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."
"StreamSets should provide a mechanism to be able to perform data quality assessment when the data is being moved from one source to the target."
"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."
Spring Cloud Data Flow is ranked 29th in Data Integration with 5 reviews while StreamSets is ranked 8th in Data Integration with 24 reviews. Spring Cloud Data Flow is rated 8.0, while StreamSets is rated 8.4. The top reviewer of Spring Cloud Data Flow writes "Provides ease of integration with other cloud platforms ". 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". Spring Cloud Data Flow is most compared with Apache Flink, Google Cloud Dataflow, Apache Spark Streaming, Azure Data Factory and Oracle Data Integrator (ODI), whereas StreamSets is most compared with Fivetran, Azure Data Factory, Informatica PowerCenter, SSIS and AWS Database Migration Service.
See our list of best Data Integration vendors.
We monitor all Data Integration reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
