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 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."
"Allows you to create API endpoints."
"The superb thing that SageMaker brings is that it wraps everything well. It's got the deployment, the whole framework."
"The few projects we have done have been promising."
"They are doing a good job of evolving."
"I have contacted the solution's technical support, and they were really good. I rate the technical support a ten out of ten."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"We've had no problems with SageMaker's stability."
"Databricks is hosted on the cloud. It is very easy to collaborate with other team members who are working on it. It is production-ready code, and scheduling the jobs is easy."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"In the manufacturing industry, Databricks can be beneficial to use because of machine learning. It is useful for tasks, such as product analysis or predictive maintenance."
"The solution offers a free community version."
"The simplicity of development is the most valuable feature."
"The fast data loading process and data storage capabilities are great."
"Databricks helps crunch petabytes of data in a very short period of time."
"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."
"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 solution requires a lot of data to train the model."
"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 suggest that Amazon SageMaker provide free slots to allow customers to practice, such as a free slot to try out working with a Sandbox."
"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."
"There are other better solutions for large data, such as Databricks."
"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."
"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."
"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."
"I have had some issues with some of the Spark clusters running on Databricks, where the Spark runtime and clusters go up and down, which is an area for improvement."
"Implementation of Databricks is still very code heavy."
"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."
"There would also be benefits if more options were available for workers, or the clusters of the two points."
"Anyone who doesn't know SQL may find the product difficult to work with."
"The data visualization for this solution could be improved. They have started to roll out a data visualization tool inside Databricks but it is in the early stages. It's not comparable to a solution like Power BI, Luca, or Tableau."
"The solution could improve by providing better automation capabilities. For example, working together with more of a DevOps approach, such as continuous integration."
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, Microsoft Azure Machine Learning Studio and Dataiku, whereas Databricks is most compared with Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio, Dremio 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.