"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."
"It is really easy to set up and the interface is easy to use."
"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."
"In StreamSets, everything is in one place."
"Ab Initio reaches the highest performance and is very flexible in processing huge amounts of data."
"The most valuable feature is real-time streaming."
"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."
"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."
"If you use JDBC Lookup, for example, it generally takes a long time to process data."
"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."
"An awesome improvement would be big data solutions, for example, implementing some kind of business intelligence or neural networks for artificial intelligence."
"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."
Ab Initio Co>Operating System is ranked 24th in Data Integration Tools with 1 review while Spring Cloud Data Flow is ranked 17th in Data Integration Tools with 2 reviews. Ab Initio Co>Operating System is rated 10.0, while Spring Cloud Data Flow is rated 8.0. The top reviewer of Ab Initio Co>Operating System writes "High performance and flexible solution for companies with large amounts of data". On the other hand, the top reviewer of Spring Cloud Data Flow writes "Good logging mechanisms, a strong infrastructure and pretty scalable". Ab Initio Co>Operating System is most compared with SSIS, Collibra Catalog, Talend Data Management Platform, AWS Glue and Oracle Data Integrator (ODI), whereas Spring Cloud Data Flow is most compared with Apache Flink, Mule Anypoint Platform, Amazon Kinesis, TIBCO BusinessWorks and Azure Data Factory.
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