We performed a comparison between Databrick and Microsoft Azure Machine Learning Studio based on our users’ reviews in four categories. After reading all of the collected data, you can find our conclusion below.
Comparison of Results: Based on the parameters we compared, Microsoft Azure Machine Learning Studio seems to be a slightly superior solution. All other things being more or less equal, our reviewers found Databricks rather expensive to purchase. Some users also feel that Microsoft Azure Machine Learning Studio has better machine learning capabilities.
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"One of the features provides nice interactive clusters, or compute instances that you don't really need to manage often."
"It is fast, it's scalable, and it does the job it needs to do."
"This solution offers a lake house data concept that we have found exciting. We are able to have a large amount of data in a data lake and can manage all relational activities."
"Databricks gives us the ability to build a lakehouse framework and do everything implicit to this type of database structure. We also like the ability to stream events. Databricks covers a broad spectrum, from reporting and machine learning to streaming events. It's important for us to have all these features in one platform."
"Databricks has helped us have a good presence in data."
"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 technical support is good."
"Their web interface is good."
"Scalability, in terms of running experiments concurrently is good. At max, I was able to run three different experiments concurrently."
"It helps in building customized models, which are easy for clients to use."
"It is very easy to test different kinds of machine-learning algorithms with different parameters. You choose the algorithm, drag and drop to the workspace, and plug the dataset into this component."
"It's good for citizen data scientists, but also, other people can use Python or .NET code."
"The product supports open-source tools."
"The interface is very intuitive."
"Anyone who isn't a programmer his whole life can adopt it. All he needs is statistics and data analysis skills."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"Implementation of Databricks is still very code heavy."
"The Databricks cluster can be improved."
"Instead of relying on a massive instance, the solution should offer micro partition levels. They're working on it, however, they need to implement it to help the solution run more effectively."
"The solution could be improved by adding a feature that would make it more user-friendly for our team. The feature is simple, but it would be useful. Currently, our team is more familiar with the language R, but Databricks requires the use of Jupyter Notebooks which primarily supports Python. We have tried using RStudio, but it is not a fully integrated solution. To fully utilize Databricks, we have to use the Jupyter interface. One feature that would make it easier for our team to adopt the Jupyter interface would be the ability to select a specific variable or line of code and execute it within a cell. This feature is available in other Jupyter Notebooks outside of Databricks and in our own IDE, but it is not currently available within Databricks. If this feature were added, it would make the transition to using Databricks much smoother for our team."
"Support for Microsoft technology and the compatibility with the .NET framework is somewhat missing."
"The product cannot be integrated with a popular coding IDE."
"The data processor can pose a bit of a challenge, but the real complexity is determined by the skill of the implementation team."
"In terms of data capabilities, if we compare it to Google Cloud's BigQuery, we find a difference. When fetching data from web traffic, Google can do a lot of processing with small queries or functions."
"Overall, the icons in the solution could be improved to provide better guidance to users. Additionally, the setup process for the solution could be made easier."
"The price could be improved."
"I have found Databricks is a better solution because it has a lot of different cluster choices and better integration with MLflow, which is much easier to handle in a machine learning system."
"I would like to see modules to handle Deep Learning frameworks."
"Stability-wise, you may face certain problems when you fail to refresh the data in the solution."
"They should have a desktop version to work on the platform."
More Microsoft Azure Machine Learning Studio Pricing and Cost Advice →
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Microsoft Azure Machine Learning Studio is ranked 2nd in Data Science Platforms with 50 reviews. Databricks is rated 8.2, while Microsoft Azure Machine Learning Studio is rated 7.6. The top reviewer of Databricks writes "A nice interface with good features for turning off clusters to save on computing". On the other hand, the top reviewer of Microsoft Azure Machine Learning Studio writes "Good support for Azure services in pipelines, but deploying outside of Azure is difficult". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Dremio and Azure Stream Analytics, whereas Microsoft Azure Machine Learning Studio is most compared with Google Vertex AI, Azure OpenAI, TensorFlow, Google Cloud AI Platform and Dataiku Data Science Studio. See our Databricks vs. Microsoft Azure Machine Learning Studio 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.