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 simplicity of development is the most valuable feature."
"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."
"The processing capacity is tremendous in the database."
"The main features of the solution are efficiency."
"The time travel feature is the solution's most valuable aspect."
"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."
"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."
"I like cloud scalability and data access for any type of user."
"The technical support is excellent, and they respond quickly."
"The features that I find to be the most valuable are the BAS (Business Analytics), the Narrate feature, and the auto-visualization."
"The ability to quickly search for and access relevant data is crucial."
"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."
"Data preparation is fantastic and fast. We were able to use multiple data sources and prepare the data quickly."
"The best feature may be data flow, which is used to prepare and clean data."
"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."
"Oracle Analytics Cloud's most valuable feature is its visualization."
"Databricks requires writing code in Python or SQL, so if you're a good programmer then you can use Databricks."
"I would like more integration with SQL for using data in different workspaces."
"The connectivity with various BI tools could be improved, specifically the performance and real time integration."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"Databricks would have more collaborative features than it has. It should have some more customization for the jobs."
"I believe that this product could be improved by becoming more user-friendly."
"The interface of Databricks could be easier to use when compared to other solutions. It is not easy for non-data scientists. The user interface is important before we had to write code manually and as solutions move to "No code AI" it is critical that the interface is very good."
"The product needs samples and templates to help invite users to see results and understand what the product can do."
"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 learning curve should be improved, and I'm uncertain if tutorials are readily available or easily accessible. We may have resorted to looking on YouTube for such information. Having easily understandable documents or guides for new users would be beneficial. AI integration would be an interesting feature to add in the next release."
"This solution could be more adaptable in its application."
"The price of the solution could be lower."
"The product should be improved in terms of connectors; right now the top twenty connectors are available, but OneDrive and Teradata are missing."
"When you implement the product on a small scale, it doesn't generate any ROI."
"One area of improvement is associated with more connectors needing to be added such as Microsoft OneDrive, Teradata and a few others. I think the list is limited to the top ones now."
"Oracle Analytics Cloud is lacking in charts. They should add more charts to it."
Databricks is ranked 1st in Data Science Platforms with 78 reviews while Oracle Analytics Cloud is ranked 9th 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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