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.

overall experience with streamsets was quite satisfying .

easy to install and use , deployment pipeline is also not complex you can use docker to install and run as container . interface is user friendly but some time is required to understand the features and how it works . i have been using data collector for ingesting data with jdbc to database . streamsets also provides logs for better monitoring , it provides many stages and libraries with them to ingest data from source to destination.

logging is complex , sometimes preview doesn't work , if you copy and use same pipeline in different server the library dependency create problems .

It was quite a nice experience working with streamsets and building ETL pipelines never felt so easy and user friendly . Streamsets is continuously growing and i think it will be more cool and user friendly with time

It is really easy to use Streamsets and the graphical interface is very user friendly which makes complex designs look easy and more understandable .

Yeah .i feel that Streamsets is continuously growing and new features are being continuously added .It lacks support for certain databases and certain functionalities are still missing.

Personally my overall experience of using the platform is very good.

I have been using DataOps platform since past 6 months and had a very good experience while creating pipelines, jobs, subscriptions and all other things. Moreover the platform is very user friendly.

One thing which I feel can be better is the time required to create a engine and restart the same.

It is very handy to ingest real-time streaming data for processing which can also scale based on the data volume. It can also be integrated with third party provider tools like snowflake, spark etc.. for data transformation.

It might be not useful for a very large real-time streaming data as it might take too much time to handle those data and transform accordingly compare to Cloud Dataflow. And another drawback I would say, it doesn't have detailed documentation.

GUI Based implementation.Can be used using Python SDK which makes it easy to use.

The functionalities keeps on changing which requires proper documentation to follow.

its ease of use and connectivity to almost all modern platform makes it a wonderful platform

Since its new there are a lot of things to improve. Such as the bugs. Streamsets should release new version more often.

Overall experience is good .

I think the feature that I like is the connectors which included in the platform.

This is not the least but the connectivity between the data container and the control hub is little bit lagging.

Setting up data pipelines is easy and straight forward, if connectors are available and not much inflight processing has to be done, one get's quick results

More complex usecases or REST endpoints often need workarounds or custom code

CDC with high-speed data streaming

Great GUI and monitoring of the data streaming pipeline

- Improve the CDC stage to allow fresh load for a particular table - Improve error handling messages

The ability to continuously integrate data with a system for alerts to know when things break is a really attractive feature.

The streamsets platform is not the fastest at times, lags or slows down occasionally.

it was a great experience using such a tool for the first time. I was amazed by what this tool can do with minimul configuration.

Ability to build pipeline within no time, with so many featurs and configurations available.

It should have third party based processors. it is trying to build all by itself.

The best part of this platform is the drag and drop GUI, it allows someone who has never built a pipeline before to understand ETL faster.

I do not have many cons about this software. I think it allows users to work faster and be more efficient with their time and resources compared to other platforms.

CI/CD with StreamSets is great. Support for multiple data platforms makes it a great choice for data integration

The logging system might be made better. If I work on a pipeline, then make a task from it, and it runs, it will continuously produce logs. Therefore, the logging system might be made simpler. The logs are now a little more challenging to comprehend and filter. It takes a while.