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."It is fast, it's scalable, and it does the job it needs to do."
"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."
"The most valuable feature is the ability to use SQL directly with Databricks."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"The integration with Python and the notebooks really helps."
"Automation with Databricks is very easy when using the API."
"Databricks' most valuable features are the workspace and notebooks. Its integration, interface, and documentation are also good."
"Easy to use and requires minimal coding and customizations."
"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 best feature may be data flow, which is used to prepare and clean data."
"It's great for consolidation and creating one source of truth."
"The specific capability I find important in Oracle Analytics Cloud is that it allows the basic user to easily drag and drop data. I also like that the solution allows the user to decide what to measure and what to see in the reports."
"It's robust. It has the ability to handle massive amounts. After reporting has been developed, there is an ease of use or a user-friendly interface for a trained workforce."
"It's valuable feature is that it is user-friendly and doesn't require much time for understanding. The solution is stable. The initial setup was straightforward."
"Analytics Cloud allows you to merge various data types and structure data from multiple sources."
"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 product cannot be integrated with a popular coding IDE."
"Would be helpful to have additional licensing options."
"The solution has some scalability and integration limitations when consolidating legacy systems."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"It would be better if it were faster. It can be slow, and it can be super fast for big data. But for small data, sometimes there is a sub-second response, which can be considered slow. In the next release, I would like to have automatic creation of APIs because they don't have it at the moment, and I spend a lot of time building them."
"It should have more compatible and more advanced visualization and machine learning libraries."
"Databricks has added some alerts and query functionality into their SQL persona, but the whole SQL persona, which is like a role, needs a lot of development. The alerts are not very flexible, and the query interface itself is not as polished as the notebook interface that is used through the data science and machine learning persona. It is clunky at present."
"The product could benefit from increased flexibility compared to other vendors."
"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."
"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."
"It is expensive."
"Sharing dataflows is not possible at this time, and the custom chart functionality is not available."
"When we have, for example, a table with low performance, we have several issues with drawing some graphics in the Oracle cloud."
"It is less scalable than Snowflake."
"At this time, dataflows cannot be shared, but I think that this should be enhanced."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Oracle Analytics Cloud is ranked 9th in BI (Business Intelligence) Tools with 24 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, Microsoft Azure Machine Learning Studio and SAS Visual Analytics, whereas Oracle Analytics Cloud is most compared with Oracle OBIEE, Tableau, Microsoft Power BI, Oracle Business Intelligence Cloud Service and SAP Analytics Cloud. See our Databricks vs. Oracle Analytics Cloud report.
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