We compared Databricks and Amazon SageMaker based on our user's reviews in several parameters.
Databricks offers seamless integration with various data sources, advanced analytics capabilities, and efficient customer service. Users appreciate the collaborative features and positive ROI. On the other hand, Amazon SageMaker is praised for its ease of use, comprehensive ML capabilities, and robust monitoring tools. Users find the pricing transparent and support team responsive.
Features: Databricks is known for its seamless integration with various data sources and platforms, collaborative capabilities, advanced analytics, and machine learning capabilities. On the other hand, Amazon SageMaker offers ease of use, comprehensive machine learning capabilities, seamless integration with other AWS services, customizable workflows, efficient model training and deployment, automated data labeling, and robust monitoring and troubleshooting tools.
Pricing and ROI: Databricks users have reported positive feedback on pricing, setup cost, and licensing. The setup cost is straightforward and hassle-free, while the license terms offer flexibility. Similarly, Amazon SageMaker users find the pricing reasonable, setup cost hassle-free, and licensing process clear and transparent., Users have reported positive outcomes and returns on investment with Databricks, appreciating its impact on efficiency, productivity, and data analysis capabilities. Similarly, Amazon SageMaker delivers positive ROI, providing value and benefits for businesses.
Room for Improvement: Databricks has room for improvement in aspects such as data visualization, monitoring and debugging tools, integration with external data sources and services, documentation and tutorials, and pricing flexibility. In comparison, users have identified areas for enhancement in Amazon SageMaker.
Deployment and customer support: Based on user reviews, there are varying durations required for deploying, setting up, and implementing a new tech solution on both Databricks and Amazon SageMaker. While some users mentioned spending three months on deployment and a week on setup for both products, it is important to evaluate the context to determine if these terms refer to the same period or should be considered separately., Customers have reported positive experiences with both Databricks and Amazon SageMaker customer service. Databricks is praised for its efficiency and proactive approach, while SageMaker is commended for its attentiveness and commitment to customer needs.
The summary above is based on 56 interviews we conducted recently with Databricks and Amazon SageMaker users. To access the review's full transcripts, download our report.
"The solution's ability to improve work at my organization stems from the ensemble model and a combination of various models it provides."
"We've had no problems with SageMaker's stability."
"The tool has made client management easier where patients need to upload their health records and we can use the tool to understand details on treatment date, amount, etc."
"The most valuable feature of Amazon SageMaker is that you don't have to do any programming in order to perform some of your use cases."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The solution is easy to scale...The documentation and online community support have been sufficient for us so far."
"We were able to use the product to automate processes."
"The processing capacity is tremendous in the database."
"The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"The solution is an impressive tool for data migration and integration."
"The initial setup is pretty easy."
"I like that Databricks is a unified platform that lets you do streaming and batch processing in the same place. You can do analytics, too. They have added something called Databricks SQL Analytics, allowing users to connect to the data lake to perform analytics. Databricks also will enable you to share your data securely. It integrates with your reporting system as well."
"We are completely satisfied with the ease of connecting to different sources of data or pocket files in the search"
"Automation with Databricks is very easy when using the API."
"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."
"The solution is complex to use."
"Lacking in some machine learning pipelines."
"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."
"SageMaker would be improved with the addition of reporting services."
"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."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"The product must provide better documentation."
"In my opinion, one improvement for Amazon SageMaker would be to offer serverless GPUs. Currently, we incur costs on an hourly basis. It would be beneficial if the tool could provide pay-as-you-go pricing based on endpoints."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"CI/CD needs additional leverage and support."
"I'm not the guy that I'm working with Databricks on a daily basis. I'm on the management team. However, my team tells me there are limitations with streaming events. The connectors work with a small set of platforms. For example, we can work with Kafka, but if we want to move to an event-driven solution from AWS, we cannot do it. We cannot connect to all the streaming analytics platforms, so we are limited in choosing the best one."
"It's not easy to use, and they need a better UI."
"In the future, I would like to see Data Lake support. That is something that I'm looking forward to."
"The integration of data could be a bit better."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
"Some of the error messages that we receive are too vague, saying things like "unknown exception", and these should be improved to make it easier for developers to debug problems."
Amazon SageMaker is ranked 5th in Data Science Platforms with 19 reviews while Databricks is ranked 1st in Data Science Platforms with 78 reviews. Amazon SageMaker is rated 7.4, while Databricks is rated 8.2. 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 Databricks writes "A nice interface with good features for turning off clusters to save on computing". Amazon SageMaker is most compared with Azure OpenAI, Google Vertex AI, Domino Data Science Platform, Dataiku and DataRobot, whereas Databricks is most compared with Informatica PowerCenter, Dataiku, Dremio, Microsoft Azure Machine Learning Studio and Azure Stream Analytics. See our Amazon SageMaker vs. Databricks report.
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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.