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."The solution is serverless so it allows us to transform data while optimizing the cost and performance of Spark jobs."
"The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features."
"The solution's technical support is good. Whenever we raise a use case where we face an issue in our company, we get a response from the solution's technical team."
"Data catalog and triggers are the two best features for me. AWS Glue has its own data catalog, which makes it great and really easy to use. Triggers are also really good for scheduling the ETL process."
"The most valuable feature of AWS Glue is its ease of use and good documentation. Additionally, we can do all the transformations that we need."
"It is a stable and scalable solution."
"One of the best features of the solution is its ability to easily integrate with other AWS services."
"It's fairly straightforward as a product; it's not very complicated."
"The Ease of configuration for pipes is amazing. It has a lot of connectors. Mainly, we can do everything with the data in the pipe. I really like the graphical interface too"
"I have used Data Collector, Transformer, and Control Hub products from StreamSets. What I really like about these products is that they're very user-friendly. People who are not from a technological or core development background find it easy to get started and build data pipelines and connect to the databases. They would be comfortable like any technical person within a couple of weeks."
"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."
"What I love the most is that StreamSets is very light. It's a containerized application. It's easy to use with Docker. If you are a large organization, it's very easy to use Kubernetes."
"It is really easy to set up and the interface is easy to use."
"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 best feature that I really like is the integration."
"In StreamSets, everything is in one place."
"The price of the solution could improve."
"On occasion, the solution's dashboard reports that a project failed due to runtime but it actually succeeded."
"The solution’s stability could be improved."
"There should be more connectors for different databases."
"The solution could be cheaper. The price of the solution is an area that needs improvement."
"It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do. For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do. It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options."
"The start-up time is really high right now. For instance, when you start up a new job, you have to wait for five or eight minutes before it starts. If the start-up time is reduced to one or two minutes, it will be great. It will be better to have a direct linkage to Redshift in AWS. If we can use data catalogs from Redshift, it will be so easy to create some data catalogs. Currently, we can only use data catalogs from S3."
"In terms of performance, if they can further optimize the execution time for serverless jobs, it would be a welcome improvement."
"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."
"We create pipelines or jobs in StreamSets Control Hub. It is a great feature, but if there is a way to have a folder structure or organize the pipelines and jobs in Control Hub, it would be great. I submitted a ticket for this some time back."
"They need to improve their customer care services. Sometimes it has taken more than 48 hours to resolve an issue. That should be reduced. They are aware of small or generic issues, but not the more technical or deep issues. For those, they require some time, generally 48 to 72 hours to respond. That should be improved."
"In terms of the product, I don't think there is any room for improvement because it is very good. One small area of improvement that is very much needed is on the knowledge base side. Sometimes, it is not very clear how to set up a certain process or a certain node for a person who's using the platform for the first time."
"Currently, we can only use the query to read data from SAP HANA. What we would like to see, as soon as possible, is the ability to read from multiple tables from SAP HANA. That would be a really good thing that we could use immediately. For example, if you have 100 tables in SQL Server or Oracle, then you could just point it to the schema or the 100 tables and ingestion information. However, you can't do that in SAP HANA since StreamSets currently is lacking in this. They do not have a multi-table feature for SAP HANA. Therefore, a multi-table origin for SAP HANA would be helpful."
"I would like to see it integrate with other kinds of platforms, other than Java. We're going to have a lot of applications using .NET and other languages or frameworks. StreamSets is very helpful for the old Java platform but it's hard to integrate with the other platforms and frameworks."
"There aren't enough hands-on labs, and debugging is also an issue because it takes a lot of time. Logs are not that clear when you are debugging, and you can only select a single source for a pipeline."
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 Confluent. See our AWS Glue vs. StreamSets report.
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