We performed a comparison between AWS Glue and StreamSets based on real PeerSpot user reviews.
Find out in this report how the two Cloud Data Integration solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Transformations are valuable because you can modify or override complex data logic from an open source or Spark to solve issues."
"The most valuable feature of AWS Glue is scalability."
"The product has a valuable feature for data catalog."
"AWS Glue's best features are scalability and cloud-based features."
"The solution integrates well with other AWS products or services."
"Its user interface is quite good. You just need to choose some options to create a job in AWS Glue. The code-generation feature is also useful. If you don't want to customize it and simply want to read a file and store the data in the database, it can generate the code for you."
"We have found it beneficial when moving data from one source to another."
"The solution is serverless so it allows us to transform data while optimizing the cost and performance of Spark jobs."
"The ability to have a good bifurcation rate and fewer mistakes is valuable."
"The best thing about StreamSets is its plugins, which are very useful and work well with almost every data source. It's also easy to use, especially if you're comfortable with SQL. You can customize it to do what you need. Many other tools have started to use features similar to those introduced by StreamSets, like automated workflows that are easy to set up."
"The most valuable feature is the pipelines because they enable us to pull in and push out data from different sources and to manipulate and clean things up within them."
"StreamSets’ data drift resilience has reduced the time it takes us to fix data drift breakages. For example, in our previous Hadoop scenario, when we were creating the Sqoop-based processes to move data from source to destinations, we were getting the job done. That took approximately an hour to an hour and a half when we did it with Hadoop. However, with the StreamSets, since it works on a data collector-based mechanism, it completes the same process in 15 minutes of time. Therefore, it has saved us around 45 minutes per data pipeline or table that we migrate. Thus, it reduced the data transfer, including the drift part, by 45 minutes."
"For me, the most valuable features in StreamSets have to be the Data Collector and Control Hub, but especially the Data Collector. That feature is very elegant and seamlessly works with numerous source systems."
"The ETL capabilities are very useful for us. We extract and transform data from multiple data sources, into a single, consistent data store, and then we put it in our systems. We typically use it to connect our Apache Kafka with data lakes. That process is smooth and saves us a lot of time in our production systems."
"In StreamSets, everything is in one place."
"It's very easy to integrate. It integrates with Snowflake, AWS, Google Cloud, and Azure. It's very helpful for DevOps, DataOps, and data engineering because it provides a comprehensive solution, and it's not complicated."
"The setup and installation is a bit complex without advanced knowledge or training."
"Currently, it supports only two languages in the background: Python and Scala. From our customization point of view, it would be helpful if it can also support Java in the background."
"Only people who can code, either in Java or Python, can use the product freely. Those who don't know Java or Python might find using AWS Glue difficult."
"The solution's visual ETL tool is of no use for actual implementation."
"Glue could perform better. It sometimes takes too long to test a Glue job. Google Cloud Platform offers more Python scripts than AWS."
"The solution could be cheaper. The price of the solution is an area that needs improvement."
"I would like to see a more robust interface on the no-code side. This would be nice to be able to split cells."
"If there's a cluster-related configuration, we have to make worker notes, which is quite a headache when processing a large amount of data."
"StreamSet works great for batch processing but we are looking for something that is more real-time. We need latency in numbers below milliseconds."
"One thing that I would like to add is the ability to manually enter data. The way the solution currently works is we don't have the option to manually change the data at any point in time. Being able to do that will allow us to do everything that we want to do with our data. Sometimes, we need to manually manipulate the data to make it more accurate in case our prior bifurcation filters are not good. If we have the option to manually enter the data or make the exact iterations on the data set, that would be a good thing."
"One area for improvement could be the cloud storage server speed, as we have faced some latency issues here and there."
"I would like to see further improvement in the UI. In addition, upgrades are not automatic and they should be automated. Currently, we have to manually upgrade versions."
"The execution engine could be improved. When I was at their session, they were using some obscure platform to run. There is a controller, which controls what happens on that, but you should be able to easily do this at any of the cloud services, such as Google Cloud. You shouldn't have any issues in terms of how to run it with their online development platform or design platform, basically their execution engine. There are issues with that."
"The data collector in StreamSets has to be designed properly. For example, a simple database configuration with MySQL DB requires the MySQL Connector to be installed."
"The logging mechanism could be improved. If I am working on a pipeline, then create a job out of it and it is running, it will generate constant logs. So, the logging mechanism could be simplified. Now, it is a bit difficult to understand and filter the logs. It takes some time."
"Sometimes, it is not clear at first how to set up nodes. A site with an explanation of how each node works would be very helpful."
AWS Glue is ranked 1st in Cloud Data Integration with 37 reviews while StreamSets is ranked 8th in Data Integration with 24 reviews. AWS Glue is rated 7.8, while StreamSets is rated 8.4. The top reviewer of AWS Glue writes "Provides serverless mechanism, easy data transformation and automated infrastructure management". On the other hand, the top reviewer of StreamSets writes "We no longer need to hire highly skilled data engineers to create and monitor data pipelines". AWS Glue is most compared with AWS Database Migration Service, Informatica PowerCenter, SSIS, Informatica Cloud Data Integration and Talend Open Studio, whereas StreamSets is most compared with Fivetran, Azure Data Factory, Informatica PowerCenter, SSIS and webMethods.io Integration. See our AWS Glue vs. StreamSets report.
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