We performed a comparison between Databricks and Qlik Sense 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 covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"Its lightweight and fast processing are valuable."
"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."
"Can cut across the entire ecosystem of open source technology to give an extra level of getting the transformatory process of the data."
"I like how easy it is to share your notebook with others. You can give people permission to read or edit. I think that's a great feature. You can also pull in code from GitHub pretty easily. I didn't use it that often, but I think that's a cool feature."
"A very valuable feature is the data processing, and the solution is specifically good at using the Spark ecosystem."
"We like that this solution can handle a wide variety and velocity of data engineering, either in batch mode or real-time."
"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."
"The data is incredibly easy to share with colleagues."
"The integration with R & Python for predictive modeling, and possibly machine learning in the future, is very valuable."
"Leveraging and reusability of already existing QlikView assets and knowledge."
"Some of the valuable areas of this solution include producing individual reports and closing the gap."
"Faster time to delivery utilizing useful charts and graphs, which allows for on-demand generation of analytical data in tables, graphs, and charts."
"For a deployment scenario, Qlik Sense is also very easy to use. I can start development from any platform, on-premise or cloud, and continue from where I left off."
"We have had an easy time supplying content to our customers."
"It connects very simply to data sources."
"The solution could be improved by integrating it with data packets. Right now, the load tables provide a function, like team collaboration. Still, it's unclear as to if there's a function to create different branches and/or more branches. Our team had used data packets before, however, I feel it's difficult to integrate the current with the previous data packets."
"The product should provide more advanced features in future releases."
"Anyone who doesn't know SQL may find the product difficult to work with."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"This solution only supports queries in SQL and Python, which is a bit limiting."
"I believe that this product could be improved by becoming more user-friendly."
"I would like to see the integration between Databricks and MLflow improved. It is quite hard to train multiple models in parallel in the distributed fashions. You hit rate limits on the clients very fast."
"Pricing is one of the things that could be improved."
"Areas for improvement include user-friendliness, self-service, and some of the visualization options for generating reports."
"The product could be improved by implementing a user-feedback ranking system."
"Multi-server deployment: not just cloud, but on premise too, and hybrid. Needs lighter protocols to communicate between the different services and with the clients."
"The SaaS offering is being enriched quickly in order to attract new customers to that space and encouraging existing customers to make the switch. The Enterprise solution is therefore lagging behind SaaS in features."
"Mostly when it comes to the loading of the data and reload times of the reports, it could be better."
"If a team wants to keep track of source code changes, there is not an out-of-the-box solution for it."
"From the initial setup of the product we had some struggles getting connections to some of our databases to function, this indicated that some of the data extract drivers can be improved and addition of some of the older database engines need to be added in Qlik Sense."
"It's going to take 20 to 30 minutes to refresh sometimes."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Qlik Sense is ranked 2nd in Data Visualization with 112 reviews. Databricks is rated 8.2, while Qlik Sense is rated 8.6. 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 Qlik Sense writes "Customizable with good ROI and a quick learning curve". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku Data Science Studio, Microsoft Azure Machine Learning Studio and Dremio, whereas Qlik Sense is most compared with Tableau, Amazon QuickSight, Microsoft Power BI, Apache Superset and TIBCO Spotfire. See our Databricks vs. Qlik Sense report.
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