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 is easy to scale...The documentation and online community support have been sufficient for us so far."
"Feature Store, CodeCommit, versioning, model control, and CI/CD pipelines are the most valuable features in Amazon SageMaker."
"The few projects we have done have been promising."
"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."
"The deployment is very good, where you only need to press a few buttons."
"We were able to use the product to automate processes."
"The most valuable feature of Amazon SageMaker is its integration. For example, AWS Lambda. Additionally, we can write Python code."
"The tool makes our ML model development a bit more efficient because everything is in one environment."
"When we have a huge volume of data that we want to process with speed, velocity, and volume, we go through Databricks."
"Databricks has improved my organization by allowing us to transform data from sources to a different format and feed that to the analytics, business intelligence, and reporting teams. This tool makes it easy to do those kinds of things."
"The most valuable feature of Databricks is the integration with Microsoft Azure."
"Databricks has helped us have a good presence in data."
"Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"The setup is quite easy."
"Easy to use and requires minimal coding and customizations."
"The load distribution capabilities are good, and you can perform data processing tasks very quickly."
"AI is a new area and AWS needs to have an internship training program available."
"The solution is complex to use."
"Lacking in some machine learning pipelines."
"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 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."
"The pricing of the solution is an issue...In SageMaker, monitoring could be improved by supporting more data types other than JSON and CSV."
"Creating notebook instances for multiple users is pretty expensive in Amazon SageMaker."
"I would say the IDE is quite immature, but it is still in its infancy, so I expect it to get better over time."
"I would like it if Databricks adopted an interface more like R Studio. When I create a data frame or a table, R Studio provides a preview of the data. In R Studio, I can see that it created a table with so many columns or rows. Then I can click on it and open a preview of that data."
"The solution has some scalability and integration limitations when consolidating legacy systems."
"There should be better integration with other platforms."
"The stability of the clusters or the instances of Databricks would be better if it was a much more stable environment. We've had issues with crashes."
"When I used the support, I had communication problems because of the language barrier with the agent. The accent was difficult to understand."
"It should have more compatible and more advanced visualization and machine learning libraries."
"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."
"I believe that this product could be improved by becoming more user-friendly."
Amazon SageMaker is ranked 5th in Data Science Platforms with 18 reviews while Databricks is ranked 1st in Data Science Platforms with 78 reviews. Amazon SageMaker is rated 7.2, 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 Data Science Studio, whereas Databricks is most compared with Informatica PowerCenter, Dataiku Data Science Studio, 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.