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."Ability to work collaboratively without having to worry about the infrastructure."
"Databricks allows me to automate the creation of a cluster, optimized for machine learning and construct AI machine learning models for the client."
"There are good features for turning off clusters."
"Databricks' most valuable feature is the data transformation through PySpark."
"The solution is very simple and stable."
"The solution is easy to use and has a quick start-up time due to being on the cloud."
"The solution offers a free community version."
"The most valuable feature of Databricks is the integration of the data warehouse and data lake, and the development of the lake house. Additionally, it integrates well with Spark for processing data in production."
"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."
"Analytics Cloud allows you to merge various data types and structure data from multiple sources."
"Data preparation is fantastic and fast. We were able to use multiple data sources and prepare the data quickly."
"It plays a crucial role in facilitating decision-making for various organizational stakeholders."
"Mobility is the most valuable feature for us. All employees can access it from anywhere. It is a big advantage for us."
"The features that I find to be the most valuable are the BAS (Business Analytics), the Narrate feature, and the auto-visualization."
"It has the best feature for data augmentation."
"It's really an enterprise solution. It has a dashboard, like standard dashboarding functionality. It also has reporting capabilities for producing pixel-perfect reports, bursting large volumes of a document if you need to. It has interactive data discovery functionality, which you would use to explore your data, bring your own data, and merge it with maybe the data from an enterprise data warehouse to get new insights from the pre-existing data. It has machine learning embedded in the solution. If you're new to machine learning, it's a really good way to get into it, because it's all within this platform, and it's really easy to use."
"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."
"Can be improved by including drag-and-drop features."
"There is room for improvement in visualization."
"Databricks' performance when serving the data to an analytics tool isn't as good as Snowflake's."
"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 solution has some scalability and integration limitations when consolidating legacy systems."
"In the next release, I would like to see more optimization features."
"The tool should improve its integration with other products."
"It is less scalable than Snowflake."
"They could improve the ease of developing the dashboard and interacting with it."
"The price of the solution could be lower."
"The product could benefit from increased flexibility compared to other vendors."
"As with most BI tools, the visualizations can be made much nicer. Currently, it has standard visualizations. They've been adding new visualizations, but we see animated visualizations from other vendors. It would be nice to have similar visualizations, such as the swarming visualizations, which are fairly new and very popular at the moment. I haven't seen that with Oracle. That would be nice."
"When you implement the product on a small scale, it doesn't generate any ROI."
"Its FAW feature has limitations in terms of usage."
"Its machine learning and visualization capabilities can be improved. There should be more visualization options."
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.
We monitor all Data Science Platforms reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.