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StreamSets, a Software AG company, eliminates data integration friction in complex hybrid and multi-cloud environments to keep pace with need-it-now business data demands. Our platform lets data teams unlock data—without ceding control—to enable a data-driven enterprise. - Resilient pipelines sense and adapt to constant changes in data structure, semantics, and infrastructure. - Learn once to create many different integration pipelines with single design experience for all patterns — streaming, batch, CDC, ETL, ELT, ML. - Reusable pipeline fragments let anyone use functionality your data engineers design. - Python SDK lets you templatize pipelines for scale by easily creating hundreds of pipelines with just a few lines of code. - Simply data transformations with pre-defined processors to meet 99% of your analytics requirements out of the box. - Topologies provide transparency to see how systems are connected and data flows across the enterprise.
Provider
Software AG
Located In
Germany
Foundation
1989
Open API
Yes
Deployment
Cloud, SaaS, Web-Based
Training
Live Online, Webinars, Documentation, Videos
Support
Email/Help Desk, FAQs/Forum, Knowledge Base, Phone Support, Chat, 24/7 (Live rep)
Our customers are the data engineering teams at enterprises who need to power modern business intelligence, AI/ML and smart applications with continuous data.
Content Source: StreamSets Platform
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StreamSets Platform Reviews
Pros
My overall experience with the StreamSets Platform has been positive. It provides powerful tools for real-time data integration.
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.
Its ease of use and connectivity to almost all modern platform makes it a wonderful platform.
CI/CD with StreamSets is great. Support for multiple data platforms makes it a great choice for data integration.
Cons
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.
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.
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.
Logging is complex , sometimes preview doesn't work , if you copy and use same pipeline in different server the library dependency create problems.
"A Powerful Tool for Real-Time Data Integration"
Overall: My overall experience with the StreamSets Platform has been positive. It provides powerful tools for real-time data integration.
Pros: What I like most about the StreamSets Platform is how easily it handles real-time data flows, making it simple to move and manage data between different systems.
Cons: Advanced features might require technical expertise, which could be challenging for non-technical teams.
"an excellent option for DataPipes development"
Overall: 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
Pros: The flexibility to deploy on different clouds the same solution
Cons: well know AWS-based sources are missed like Sales ads & seller partner
"Decent Tool With a Some Tweaks to Make"
Overall: 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.
Pros: I enjoy the well built user interface and the ability to see the data in flight in the pipeline.
Cons: 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.
"Build ETL pipeline with StreamSets Platform"
Pros: 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.
Cons: 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.
"A Handy Platform for ETL and ELT."
Pros: 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.
Cons: 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.
