IBM StreamSets Reviews & Product Details

Listed are the things which I liked most about Streamset -

a. Presence of inbuilt connectors (in-preise version) which can useful in using it for almost every source/target systems.

b. The is GUI is user friendly and it has certainly helped my platform team to create the streaming data pipeline faster )Previously we were using pyspark)

c. Alongwith tool, the Streamset support team is also excellent.

d. The availability of streamsets academy through which we an get our resources trained easily. Review collected by and hosted on G2.com.

There are lesser number of connectors available in the cloud version of Streamsets.

The inability to supports "exactly once" delivery of data creates limitation in few of the use cases.Although we have managed this through workaround but having ths ability in Streamsets will certainly help. Review collected by and hosted on G2.com.

UI of the tool is very easy to understand even for a beginner. It has the graphical pipeline feature to convert source data and add some processing steps on top it and then send it to target system. It seems simple to implement from a docker image in your environment. Opening the tool in your chrome or any web browser is less heavy in terms of RAM usage and logging in and log out times are quick. Review collected by and hosted on G2.com.

It still seems a bit under matured in terms of support for more 3rd party vendors like SAP, Salesforce, etc. Review collected by and hosted on G2.com.

I love the StreamSets UI and its interface. The components in StreamSets are very useful and very easy to use. You can esily implement a pipeline using the desired origin from the lits of various origins. You can use it on daily basis for your pipeline review. The customer support from the StreamSets side is very appreciated. Review collected by and hosted on G2.com.

There is nothing to say bad about it. Just sometimes the preview field lacks in previeing the high intensity data. Review collected by and hosted on G2.com.

I used StreamSets in one of my ETL project where we were working on Apache Spark for handling bulk data. The best thing I feel is it provides an easy way to configure pipelines so that we were able to process tasks easily using the same. Also apart from this it also helped in monitoring the same. Even for beginners it is very easy to learn. Review collected by and hosted on G2.com.

While doing performance testing it took a lot of time for around 8-10 million records. That could be improved further. Also I think there is a scope to improve the details of errors in a more detailed way. Review collected by and hosted on G2.com.

StreamSets makes designing and managing data pipelines easy-peasy. Its versatility and vast array of connectors simplify the process of handling different data sources. It's like having a smooth operator for your data flow. Review collected by and hosted on G2.com.

While StreamSets offers many benefits, some users find its learning curve a bit steep initially. Additionally, the complexity of certain configurations might be overwhelming for beginners. Review collected by and hosted on G2.com.

The tool has userfriendly interface, which has simplified the process of designing data pipelines. The number of connectors has made it easy for me to integrate various data sources. Also an additional thing is that it can handle both stream and batch data. In the organisation, the client has migrated to StreamSets and it is used almost 80% of the time. The team is quite accessible in case of any defects raised and is very co-orperative. Review collected by and hosted on G2.com.

Down sides of using SteamSets might be the cost, for some scenarios the solution might be costlier than the competition, For Large scale data there have been reports of performance issues. Review collected by and hosted on G2.com.

Streamsets is a good and lightweight integration tool with good ease of integration. It's fast and reliable. It has a decent library of connectors which are easy to use. I have been using streamsets for a year now and recently switched to data bricks. Customer support turnaround is decent. Ease of implementation is not that good as the learning curve is high without a good resource to study from Review collected by and hosted on G2.com.

lack of documentation and community support Review collected by and hosted on G2.com.

It's very easy to build pipelines. Just drag and drop components and the coding can we done within the components. We can use a wide range of tech and languages to get our data transformation done. We can also connect to different DBs. Also modifying the pipelines and deploying it is very easy. Review collected by and hosted on G2.com.

Probably if we make a change to any jar components in the spark evaluator or add any component to the files in StreamSets , we need to restart the server. Review collected by and hosted on G2.com.

I've had a very positive overall experience with streamsets. Streamsets are a tool that our organization uses to migrate its on-premises data to the cloud, it is a potent tool that can assist us in our cloud migration journey by connecting to numerous tools, such as Hadoop and Teradata, with ease. Review collected by and hosted on G2.com.

So far i have observed that debugging becomes difficult when utilizing a large dataset because the pipeline fails without generating a specific error. It cannot establish parallel connections with Teradata to speed up pipeline execution. Review collected by and hosted on G2.com.

Streamsets is having functions to built,check,observe and change the data pipeline which are momently providing data. Review collected by and hosted on G2.com.

StreamSets can handle a lot of data sources and destinations, but it struggles a bit with some fancy proprietary systems. Review collected by and hosted on G2.com.