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."Imageflow is a visual tool that helps make it easier for business people to understand complex workflows."
"The built-in optimization recommendations halved the speed of queries and allowed us to reach decision points and deliver insights very quickly."
"It's great technology."
"Databricks is based on a Spark cluster and it is fast. Performance-wise, it is great."
"The capacity of use of the different types of coding is valuable. Databricks also has good performance because it is running in spark extra storage, meaning the performance and the capacity use different kinds of codes."
"There are good features for turning off clusters."
"Databricks covers end-to-end data analytics workflow in one platform, this is the best feature of the solution."
"I work in the data science field and I found Databricks to be very useful."
"Oracle Analytics Cloud's most valuable feature is its visualization."
"The solution is user-friendly."
"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."
"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."
"Data preparation is fantastic and fast. We were able to use multiple data sources and prepare the data quickly."
"Mobility is the most valuable feature for us. All employees can access it from anywhere. It is a big advantage for us."
"The best feature may be data flow, which is used to prepare and clean data."
"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."
"If I want to create a Databricks account, I need to have a prior cloud account such as an AWS account or an Azure account. Only then can I create a Databricks account on the cloud. However, if they can make it so that I can still try Databricks even if I don't have a cloud account on AWS and Azure, it would be great. That is, it would be nice if it were possible to create a pseudo account and be provided with a free trial. It is very essential to creating a workforce on Databricks. For example, students or corporate staff can then explore and learn Databricks."
"Implementation of Databricks is still very code heavy."
"I would like more integration with SQL for using data in different workspaces."
"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."
"The product should incorporate more learning aspects. It needs to have a free trial version that the team can practice."
"Pricing is one of the things that could be improved."
"Databricks may not be as easy to use as other tools, but if you simplify a tool too much, it won't have the flexibility to go in-depth. Databricks is completely in the programmer's hands. I prefer flexibility rather than simplicity."
"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."
"It is less scalable than Snowflake."
"It should simplify data connectivity and modeling, making data extraction more streamlined and adaptable for diverse use cases."
"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."
"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."
"Analytics Cloud allows you to merge various data types and structure data from multiple sources."
"Its FAW feature has limitations in terms of usage."
"It is expensive."
"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."
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 Data Science Studio, Microsoft Azure Machine Learning Studio and Confluent, 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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