StreamSets Data Collector
StreamSets | 3.22.3Linux/Unix, Amazon Linux Amazon Linux 2 - 64-bit Amazon Machine Image (AMI)
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StreamSets data pipelines
What do you like best about the product?
StreamSets has lot of out of box features to use for data pipelines and connect AWS Kinesis, DB or Kafka and send to HDFS & Hive.
What do you dislike about the product?
Some of features like Aerospike connectors has deprecated and MapReduce has issues in control hub running in cluster out of Cloudera.
What problems is the product solving and how is that benefiting you?
Easy integration with big data tools and real time data ingestion.
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Data transformation workflow in LCNC model
What do you like best about the product?
It is very easy with LCNC framework and every workflow creation is about drag and drop of the components. It's really reduce lot of developement efforts. Anyone can operate from Day1.
What do you dislike about the product?
SDC needs much more configurable parameters to maintain the buffer config etc. It's always must to have to tool tip on when needed and keep more guided at places in chosing the component
What problems is the product solving and how is that benefiting you?
Event framework and processing events in lightspeed is amazing and i would see streamset gives me a option to see my real time metrics gathered from multiple source got into visibility with lot of transformations.
Used to be great, went downhill 2 years ago
What do you like best about the product?
There are still remnants of good time when Streamsets was a modern, open and friendly solution
What do you dislike about the product?
Failed to recognize product strengths and develop, instead prioritized monetization. Lack of product vision and direction by company management is evident. And support is subpar.
What problems is the product solving and how is that benefiting you?
Running EL and ETL
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.
StreamSets Data Collector & Transformer Review
What do you like best about the product?
Easy to learn and use for complex ETL processes.
What do you dislike about the product?
Fewer support documents online other than documentation.
What problems is the product solving and how is that benefiting you?
Below are the problems I have solved.
1. Data Collector: Collected data from on-premise sources to the cloud.
2. Applied transformations to prepare data for analytics.
1. Data Collector: Collected data from on-premise sources to the cloud.
2. Applied transformations to prepare data for analytics.
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 review
What do you like best about the product?
Build fast and efficient data pipelines . Setting up environment is not complex and can be done within minutes.
What do you dislike about the product?
Lag on streamets cloud UI , not enough use cases and examples for many problems.Hybrid Data sets cannot be joined in data collector which is a significant limitation. Hands on lab has lesser time duration to practice.
What problems is the product solving and how is that benefiting you?
Integrating API and speeding up build.
Pleasantly surprised by its capabilities.
What do you like best about the product?
The UI canvas and choosing the different stages like processors and origin and destinations.
What do you dislike about the product?
The lineage/provenance feature needs work. I hate to compare it with Apache Nifi but this is one feature that Nifi trumps Stream Sets on.
What problems is the product solving and how is that benefiting you?
We have a lot of different formats of data and transforming it using hand-coded ETL tools or other systems is cumbersome and frustrating. Stream sets does things elegantly and in a manner that is least time-consuming.
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
Review of Working in StreamSets platform
What do you like best about the product?
The intuitive canvas for designing all the Streamsets pipelines coupled with the ease of configuration of environment values in Streamsets are very useful for a Data Architect.
What do you dislike about the product?
the datacollector has to be designed properly. Few of the components require external jars . For example a simple DB configuration like MySQLDB requires the jar for mysql connector to be installed in the Datacollector in order to use tht Datacollector for data reading purposes.
What problems is the product solving and how is that benefiting you?
Problems related to Data Analytics and real-time predictions for various real-life business use cases. This has helped in generating new business ideas and predictions of solutions.
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