We performed a comparison between Google Cloud Datalab and Oracle Analytics Cloud based on real PeerSpot user reviews.
Find out in this report how the two Data Visualization solutions compare in terms of features, pricing, service and support, easy of deployment, and ROI."The APIs are valuable."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"All of the features of this product are quite good."
"Google Cloud Datalab is very customizable."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"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 technical support is excellent, and they respond quickly."
"The solution can scale."
"The AI/ML enablement is useful, as many reporting tools do not offer machine learning models as of now, without writing customized code."
"Analytics Cloud allows you to merge various data types and structure data from multiple sources."
"The features that I find to be the most valuable are the BAS (Business Analytics), the Narrate feature, and the auto-visualization."
"The technical support services are good."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The product must be made more user-friendly."
"The interface should be more user-friendly."
"There is room for improvement in the graphical user interface. So that the initial user would use it properly, that would be a good option."
"We have also encountered challenges during our transition period in terms of data control and segmentation. The management of each channel and data structure as it has its own unique characteristics requires very detailed and precise control. The allocation should be appropriate and the complexity increases due to the different time zones and geographic locations of our clients. The process usually involves migrating the existing database sets to gcp and ensure data integrity is maintained. This is the only challenge that we faced while navigating the integers of the solution and honestly it was an interesting and unique experience."
"Sharing dataflows is not possible at this time, and the custom chart functionality is not available."
"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."
"The price of the solution could be lower."
"The interfaces could be improved and some in-memory operations could be built in."
"It should simplify data connectivity and modeling, making data extraction more streamlined and adaptable for diverse use cases."
"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."
"At this time, dataflows cannot be shared, but I think that this should be enhanced."
"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."
Google Cloud Datalab is ranked 20th in Data Visualization with 5 reviews while Oracle Analytics Cloud is ranked 6th in Data Visualization with 23 reviews. Google Cloud Datalab is rated 7.6, while Oracle Analytics Cloud is rated 8.0. The top reviewer of Google Cloud Datalab writes "Easy to setup, stable and easy to design data pipelines". 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". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, IBM SPSS Modeler and KNIME, whereas Oracle Analytics Cloud is most compared with Databricks, Oracle OBIEE, Tableau, Microsoft Power BI and Oracle Business Intelligence Cloud Service. See our Google Cloud Datalab vs. Oracle Analytics Cloud report.
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