We performed a comparison between Databricks and Oracle Analytics Cloud 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."The most valuable feature of Databricks is the notebook, data factory, and ease of use."
"Databricks has helped us have a good presence in data."
"Databricks gives you the flexibility of using several programming languages independently or in combination to build models."
"The solution is built from Spark and has integration with MLflow, which is important for our use case."
"Databricks' Lakehouse architecture has been most useful for us. The data governance has been absolutely efficient in between other kinds of solutions."
"The fast data loading process and data storage capabilities are great."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"The setup is quite easy."
"The technical support is excellent, and they respond quickly."
"The solution can scale."
"It's great for consolidation and creating one source of truth."
"A valuable feature is the speed of the solution."
"The advanced calculations by the tool are highly effective"
"It has the best feature for data augmentation."
"I've discovered that the new layout of this product makes Docker sharing, machine learning support, and data backups more efficient. Unlike the older method of linking physical, pre-logical, and presentation layers separately, the new interface simplifies this process. Additionally, the integration of databases and machine learning is seamless, with the new visualization approach being particularly beautiful and highly beneficial."
"The solution is user-friendly."
"Databricks would benefit from enhanced metrics and tighter integration with Azure's diagnostics."
"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."
"I would like it if Databricks made it easier to set up a project."
"Databricks is an analytics platform. It should offer more data science. It should have more features for data scientists to work with."
"It's not easy to use, and they need a better UI."
"I have seen better user interfaces, so that is something that can be improved."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"It should simplify data connectivity and modeling, making data extraction more streamlined and adaptable for diverse use cases."
"The migration of older dash tools from the classic interface of Oracle BI prior to OAS launch to the newer Data Visualization and Oracle Analytics Cloud interfaces, including dashboards and metadata, is currently a cumbersome process. Improvements in this area would be highly beneficial. Additionally, the administration of the cloud, particularly the startup of services and linking of the WebLogic server and integrated components, takes longer than desired. In today's enterprise landscape, waiting forty minutes for the server to be operational is quite lengthy; ideally, this process should take a maximum of four minutes. It would be excellent to incorporate metadata management as an integral part of the Oracle Analytics Cloud. When dealing with integrated data from various sources, tracking data lineage and the entire data life cycle, from sources to report development and the mapping of reports to specific dashboards, should be seamlessly managed within the Oracle Analytics Cloud. This would eliminate the need for additional tools. Drawing a comparison, tools like Tableau have a feature enabling metadata management, making it easier to trace the complete data lineage of reports. Managing over seven hundred and thirty-six business dashboards, the metadata management capability within Tableau simplified the process of understanding how reports were developed, including details like associated tables, users, linked views, materialized views, data segmentations, ETL jobs, and the data warehouse stages. Enhancing metadata tracking within the Oracle Analytics Cloud layout would facilitate easy and practical management of the complete data life cycle, encompassing user accessibility and report permissions."
"At this time, dataflows cannot be shared, but I think that this should be enhanced."
"The price of the solution could be lower."
"When you implement the product on a small scale, it doesn't generate any ROI."
"Sharing dataflows is not possible at this time, and the custom chart functionality is not available."
"The product should be improved in terms of connectors; right now the top twenty connectors are available, but OneDrive and Teradata are missing."
"The product should improve its user interface. It should be welcoming and modern. Developers should also find it easier to build data models. Oracle Analytics Cloud needs to have better visualizations and more options in the marketplace."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Oracle Analytics Cloud is ranked 8th in BI (Business Intelligence) Tools with 25 reviews. Databricks is rated 8.2, while Oracle Analytics Cloud is rated 8.0. 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 Oracle Analytics Cloud writes "Reliable, capable of handling massive amounts of data, and good value for money". Databricks is most compared with Amazon SageMaker, Informatica PowerCenter, Dataiku, Dremio and Confluent, whereas Oracle Analytics Cloud is most compared with Oracle OBIEE, Microsoft Power BI, Tableau, Oracle Business Intelligence Cloud Service and SAP Analytics Cloud. See our Databricks vs. Oracle Analytics Cloud report.
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