We performed a comparison between Amazon SageMaker and Starburst Enterprise based on real PeerSpot user reviews.
Find out what your peers are saying about Databricks, Microsoft, Alteryx and others in Data Science Platforms."They are doing a good job of evolving."
"We've had experience with unique ML projects using SageMaker. For example, we're developing a platform similar to ChatGPT that requires models. We utilize Amazon SageMaker to create endpoints for these models, making accessing them convenient as needed."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The Autopilot feature is really good because it's helpful for people who don't have much experience with coding or data pipelines. When we suggest SageMaker to clients, they don't have to go through all the steps manually. They can leverage Autopilot to choose variables, run experiments, and monitor costs. The results are also pretty accurate."
"We were able to use the product to automate processes."
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"The deployment is very good, where you only need to press a few buttons."
"The product aggregates everything we need to build and deploy machine learning models in one place."
"We have noticed improvements in performance using Starburst Enterprise. It handles complex data, including reading and partitioning files. We can add a new catalog to Starburst Enterprise by providing connection details and service account information. This allows us to integrate with existing tools, such as the Snowflake database, which we use for data protection in our project."
"In general, improvements are needed on the performance side of the product's graphical user interface-related area since it consumes a lot of time for a user."
"The training modules could be enhanced. We had to take in-person training to fully understand SageMaker, and while the trainers were great, I think more comprehensive online modules would be helpful."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"Scalability to handle big data can be improved by making integration with networks such as Hadoop and Apache Spark easier."
"Amazon SageMaker could improve in the area of hyperparameter tuning by offering more automated suggestions and tips during the tuning process."
"The payment and monitoring metrics are a bit confusing not only for Amazon SageMaker but also for the range of other products that fall under AWS, especially for a new user of the product."
"SageMaker would be improved with the addition of reporting services."
"The product must provide better documentation."
"Starburst Enterprise could improve by offering additional features similar to those provided by other SQL query tools. For example, incorporating functionalities like pivot tables would make it more feasible to use."
Earn 20 points
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Starburst Enterprise is ranked 14th in Data Science Platforms with 1 review. Amazon SageMaker is rated 7.4, while Starburst Enterprise is rated 8.0. The top reviewer of Amazon SageMaker writes "Easy to use and manage, but the documentation does not have a lot of information". On the other hand, the top reviewer of Starburst Enterprise writes "Handles complex data and improves performance ". Amazon SageMaker is most compared with Databricks, Azure OpenAI, Google Vertex AI, Domino Data Science Platform and Dataiku, whereas Starburst Enterprise is most compared with Dremio, Starburst Galaxy, Alteryx, Databricks and Apache Flink.
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We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
