Easy to use (for experienced users)FlexibilityEscalabilityCloud NativeMulticloud

It can be really complex to understand for someone not realated to data procesing

Platform where you can develop all in your on-premises and do a cloud deployment when you're ready right away, to us that a key feature controlling our cloud budget

The flexibility to deploy on different clouds the same solution

well know AWS-based sources are missed like Sales ads & seller partner

The tool requires a somewhat intimate knowledge with the JVM. In addition to this, it seemed a bit difficult to integrate into our CI-CD process as well.

I enjoy the well built user interface and the ability to see the data in flight in the pipeline.

It proved to be very difficult to extend a simple pipeline to accomodate a different scenarios. In addition, common stuff like CDC seemed a bit difficult to set up.

Good but local instance connection that is connectivity between cloud installed Streamsets platform and local messaging instance such as Kafka etc is a real pain. Hope Streamsets comes up with a easy going solution for the same.

The intuitive GUI of the Streamsets DataOps Platform is really useful and user friendly. The low code - No Code Process Pipeline creation based approach is really helpful for developers to build data pipelines. The connectors are also really helpful as they provide just drag and drop approach to build the pipeline and only do some configurations in order to implement them. Then comes the monitoring tool which helps in monitoring the pipeline and the Jobs in realtime which is very informative and easily understandable. The Strigo platform is also helpful as because it helps in quick setup of the docker instance so that the data collector can be configured quickly to capture realtime data.

It's that the manual installation of docker instance and manual setups of all the needed connectors in the local desktop instance if the streamsets is used without Strigo which is a real pain. Hope Streamsets comes up with a clear and precise documentation for the same. Also connectivity between local docker instance(where data collector is set up) and applications installed outside cloud and on premise such as Kafka instance(installed on premise) is a real pain . Hope Streamsets comes up with an easy and comfortable feature to use with proper documentation.

I not only love how easily configurable are stages of StreamSets but also other functionality provided by StreamSets. i,e SDK. I have mostly worked on SDK part lately. I also liked streamSets provided 3 engine type for use and provided especially different engine for Snowflake. Streamsets provided handy easily configurable connectors for many famous sources and destinations, which mostly worked in favor of ETL developer. Data drift is main feature of Streamsets.

As many connectors are provided, but Streamsets also don.t have connectors for many sources and destination. Also SDK kit doesn't have many things not mentioned in documentation. How to configured stages in SDK? but with small work that can be found.