"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."
"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 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."
"In StreamSets, everything is in one place."
"The most valuable features are that is easy to install and it is user-friendly."
"It is fully featured. It has allowed me to do everything I wanted to do."
"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."
"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."
"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."
"Pricing is an area that needs improvement."
"The user interface could improve. We are looking at some cloud-based databases, and I don't think they support that."
HiT Software DBMoto is ranked 9th in Data Replication with 1 review while JumpMind SymmetricDS is ranked 5th in Data Replication with 1 review. HiT Software DBMoto is rated 8.0, while JumpMind SymmetricDS is rated 9.0. The top reviewer of HiT Software DBMoto writes "User-friendly, easy to manage, with good support". On the other hand, the top reviewer of JumpMind SymmetricDS writes "Fully-featured, good performance, and easy to use and install". HiT Software DBMoto is most compared with Syniti Data Replication, Qlik Replicate, Oracle GoldenGate, SSIS and Kofax RPA, whereas JumpMind SymmetricDS is most compared with Oracle GoldenGate, SSIS, AWS Database Migration Service, EDB Postgres Replication Server and Spring Cloud Data Flow.
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