StreamSets Data Collector
StreamSets | 3.22.3Linux/Unix, Amazon Linux Amazon Linux 2 - 64-bit Amazon Machine Image (AMI)
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Powerful and user-friendly data integration platform
What do you like best about the product?
The best feature of StreamSets is its intuitive visual interface, allowing us to effortlessly design, monitor, and manage data pipelines without the need for complex coding. This has significantly reduced our development time and made the process highly accessible to both technical and non-technical team members.
What do you dislike about the product?
Though StreamSets is an outstanding tool, one aspect that could be improved is the initial learning curve for new users. While the interface is user-friendly, understanding all the features and configurations may take some time for those unfamiliar with data integration platforms. However, the support documentation and community forum help to mitigate this issue to a large extent.
What problems is the product solving and how is that benefiting you?
StreamSets has been a game-changer for us, addressing several critical challenges in our data management process. Firstly, it simplifies data integration tasks through its intuitive visual interface, enabling both technical and non-technical team members to participate in designing, monitoring, and managing data pipelines. This has significantly reduced the learning curve and development time, improving our overall productivity.
Furthermore, StreamSets has helped us improve data quality and governance. Its monitoring and validation features allow us to track data quality metrics, identify anomalies, and ensure compliance with data privacy regulations and industry standards. By ensuring high-quality data, we can make more accurate and reliable business decisions.
Moreover, as our data volumes grow, StreamSets scales effortlessly, handling large-scale data processing without compromising on performance. This scalability has allowed us to handle increasing data demands and grow our business without worrying about data integration bottlenecks.
Furthermore, StreamSets has helped us improve data quality and governance. Its monitoring and validation features allow us to track data quality metrics, identify anomalies, and ensure compliance with data privacy regulations and industry standards. By ensuring high-quality data, we can make more accurate and reliable business decisions.
Moreover, as our data volumes grow, StreamSets scales effortlessly, handling large-scale data processing without compromising on performance. This scalability has allowed us to handle increasing data demands and grow our business without worrying about data integration bottlenecks.
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Best Data Pipeline Building Platform
What do you like best about the product?
Stream Set is one of the leading Data Pipeline creating platforms and it is used by many tech giants also. Also, it is partnered with AWS, Snowflake, Google Cloud, and Azure. Which is very help full for Devops, Dataops and Data engineers. because it provides a comprehensive solution on one platform.
What do you dislike about the product?
I think it didn't have any downfall because the platform is so versatile. The only thing they can improve is by adding more regional servers around the world so that latency will reduce.
What problems is the product solving and how is that benefiting you?
I want to connect my Apache Kafka and Apache Nifi with data lake so I found this Platform and it really helped me, because of this amazing platform my work got complete in few click only.
Streamsets is a great product for dataops.
What do you like best about the product?
the ability to create a pipeline with with visual representation of the excecutions.
What do you dislike about the product?
this training provided is very basic and could be more specific.
What problems is the product solving and how is that benefiting you?
data engineering
Very Powerful and Easy Data Engineering platform. Capable to handle multiple platform and huge data.
What do you like best about the product?
StreamSets is very light. Since it is containerized app, it is easy to use with Docker if you are an individual developer. For organizations they can use Kubernetes.
They have a very easy and user-friendly user interface. It takes only a few days for new developers to start and deploy their first pipelines.
StreamSets provides easy and powerful stages(kind of connectors) to integrate StreamSets with different platforms such as Kafka, SalesForce, Oracle DB, Rest API, HTTPS connection, Data lakes and many more.
StreamSets uses regex expression for data transformation related operation which is really easy.
Monitoring StreamSets pipelines are very easy, you can register your Data collector to control hub using provisioning agents. After registering you can deploy pipelines to SCH and create jobs. All of this can be done using their Python SDK which can easily be integrated with ADO release pipelines.
After creating/deploying pipelines users can use SCH subscription to create alerts if pipelines/jobs changes their status.
For individual alerts pipeline have built-in capability to do so.
After their version 4.0.1 , sdc are merged with their data ops platform. This allows individual developers to have the feel of a Control Hub. It also remove platform dependancy.
They have very excellent security. Pipeline can be integrated with Azure Keyvaults which eliminates the needs of sharing credentials with Developers. Same goes for parametrs and runtime parameter. Developers can easily replace any value in pipeline with ADO library variables.
If you are an Organization they provide very extensive support, work instantly on any bug if found by an organization. They also have customer success team which will do anything to make sure your organisation's experience with StreamSets is seamless.
They have a very easy and user-friendly user interface. It takes only a few days for new developers to start and deploy their first pipelines.
StreamSets provides easy and powerful stages(kind of connectors) to integrate StreamSets with different platforms such as Kafka, SalesForce, Oracle DB, Rest API, HTTPS connection, Data lakes and many more.
StreamSets uses regex expression for data transformation related operation which is really easy.
Monitoring StreamSets pipelines are very easy, you can register your Data collector to control hub using provisioning agents. After registering you can deploy pipelines to SCH and create jobs. All of this can be done using their Python SDK which can easily be integrated with ADO release pipelines.
After creating/deploying pipelines users can use SCH subscription to create alerts if pipelines/jobs changes their status.
For individual alerts pipeline have built-in capability to do so.
After their version 4.0.1 , sdc are merged with their data ops platform. This allows individual developers to have the feel of a Control Hub. It also remove platform dependancy.
They have very excellent security. Pipeline can be integrated with Azure Keyvaults which eliminates the needs of sharing credentials with Developers. Same goes for parametrs and runtime parameter. Developers can easily replace any value in pipeline with ADO library variables.
If you are an Organization they provide very extensive support, work instantly on any bug if found by an organization. They also have customer success team which will do anything to make sure your organisation's experience with StreamSets is seamless.
What do you dislike about the product?
A few of the stages are a bit unstable. Like Oracle CDC client. They work fine but in some corner case scenario, it becomes a bit tricky. Logging mechanism is excellent and extensive but it could be simpler.
What problems is the product solving and how is that benefiting you?
I am in an organization where we are working on sharing Data between mutiple application running on different platoform. So we needed a tool/platform with can easily integrate with variety of technology and can adopt with this everchanging era.
StreamSets allowed us to share real time data between platfoms which also removed dependancy from heavier ETL tools like SSIS, Abinitio.
Since it is easier which allows our talent developement team enable our developers to use StreamSets.
StreamSets allowed us to share real time data between platfoms which also removed dependancy from heavier ETL tools like SSIS, Abinitio.
Since it is easier which allows our talent developement team enable our developers to use StreamSets.
Excellent and Useful Engine for Everything data
I have been using streamsets for a while now and I can say this is a very powerful design and execution engine. Makes it easy of me to create pipelines, seamless transition from s3 specifically to my Kafka and all. This is very good and will highly recommend
streamsets review
best datastreaming app in aws marketplace, and im using it every time, and my experience is very good so it is highly recommended by me
StreamSets
It is one of best service, it is a lightweight, powerful design and execution engine that streams data in real time. Data Collector provides a web-based user interface (UI) to configure pipelines, preview data, monitor pipelines, and review snapshots of data.
Makes Life Easy
Data Migration cross RDBMS and NO-SQL become very easy.
What do you like best about the product?
I found it very flexible and GUI-based configuration makes it very user-friendly.
What do you dislike about the product?
So good so far, didn't find anything wrong about streamsets as of now.
What problems is the product solving and how is that benefiting you?
Data Migration from RDBMS to RDBMS and RDBMS to NO-SQL.
By using StreamSets I am able to migrate data without any downtime and without any help from DBA. in the traditional way we were doing import and export for RDBMS to RDBMS which is not now needed. from RDBMS to NO-SQL I was using custom scripts to export data in CSV from Oracle and import it in Cassandra but now I have created a pipeline and all work is sorted now.
By using StreamSets I am able to migrate data without any downtime and without any help from DBA. in the traditional way we were doing import and export for RDBMS to RDBMS which is not now needed. from RDBMS to NO-SQL I was using custom scripts to export data in CSV from Oracle and import it in Cassandra but now I have created a pipeline and all work is sorted now.
Lead Data Engineer
What do you like best about the product?
The development speed for a Spark Application.
What do you dislike about the product?
The control hub must be available as part of trail version, with minimal feature
What problems is the product solving and how is that benefiting you?
Convert Spark coding into drag and dropable UI
Recommendations to others considering the product:
If you want to exploit the full power of Apache Spark and maintain it easily then Streamsets in the best way to do it.
Been using Streamsets for all of use cases for onprem to cloud transfers
What do you like best about the product?
Easy UX makes it easier to configure pipelines
What do you dislike about the product?
Streamsets Control hub has a lot of issues when multiple DC attached
What problems is the product solving and how is that benefiting you?
Onprem to Cloud data transfers
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