We performed a comparison between Databricks and Tableau based on real PeerSpot user reviews.
Find out in this report how the two Data Science Platforms solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"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."
"Specifically for data science and data analytics purposes, it can handle large amounts of data in less time. I can compare it with Teradata. If a job takes five hours with Teradata databases, Databricks can complete it in around three to three and a half hours."
"I haven't heard about any major stability issues. At this time I feel like it's stable."
"The ability to stream data and the windowing feature are valuable."
"The main features of the solution are efficiency."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"The ability to deploy is the added ability to centralise the Tableau repository for all Tableau Developers."
"It provides supporting data for critical policy and operational changes"
"Tableau is highly scalable. Now that they've introduced Hyper, you can create an extract of more than 5 million rows in minutes and then do your analysis."
"The most valuable feature is that we can integrate with our own database, and it will displays the KPIs. This is highly required from the business side."
"It's very easy to visualize data with this product. The visualization maps of and frames that we have been able to cross-reference has been excellent."
"The dashboards are amazing, with different report types and stunning visuals. Most importantly, Tableau's AI with machine learning automatically predicts features and reports based on historical data. These are the three most valuable features for me."
"From my perspective, it enables clients to better understand our data and make better decisions based on that information."
"It is easy to adapt visualizers to have interactive conversations among decision-makers."
"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."
"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."
"Generative AI is catching up in areas like data governance and enterprise flavor. Hence, these are places where Databricks has to be faster."
"The product should provide more advanced features in future releases."
"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 initial setup is difficult."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"In the next release, I would like to see more optimization features."
"In the next release, I would like to be able to have the option to see more raw data that I'm converting on the dashboard."
"I take a long time to process the hundreds of thousands or millions of records that must be processed every day."
"Tableau could be improved by introducing a data manipulation layer within the tool itself. Currently, data manipulations require using additional tools like Alteryx. If Tableau included these capabilities, it would reduce the need for external dependencies. The tool gets slower when we feed huge amounts of data."
"When there are millions of records, scaling up is quite difficult."
"We need big servers to perform the operations that we are doing. They should probably relook at its architecture."
"Many things have to be improved in Tableau. Right now, we make the calculation, and then we get that information. It would be better if business users could do that. I would ask the people at Tableau to provide that option to business users to get that information in one click. It would be better if they automated some calculations. There should be more automation in Tableau. However, there are many things in automation mode, but it is very limited at the moment. We need automation for people who do not know much about Tableau. It would also be better if there were good community support like in Alteryx."
"I would like Tableau Prep to be integrated with Tableau Desktop. I would also like more customizations for tables."
"The Hyper Extract functionality is not as strong as that provided by Microsoft SQL."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Tableau is ranked 2nd in BI (Business Intelligence) Tools with 293 reviews. Databricks is rated 8.2, while Tableau is rated 8.4. 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 Tableau writes "Provides fast data access with in-memory extracts, makes it easy to create visualizations, and saves time". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Microsoft Azure Machine Learning Studio and Microsoft Power BI, whereas Tableau is most compared with Microsoft Power BI, Amazon QuickSight, Domo, SAS Visual Analytics and SAP Analytics Cloud. See our Databricks vs. Tableau report.
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