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 infrastructure is highly reliable and efficient, contributing to a positive experience."
"All of the features of this product are quite good."
"In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"The APIs are valuable."
"Google Cloud Datalab is very customizable."
"Analytics Cloud allows you to merge various data types and structure data from multiple sources."
"The product is easily customized."
"The technical support is excellent, and they respond quickly."
"The solution can scale."
"The most valuable features of the solution are dashboarding and data visualization."
"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."
"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."
"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."
"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."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"The interface should be more user-friendly."
"The product must be made more user-friendly."
"Its FAW feature has limitations in terms of usage."
"The solution could be more flexible."
"Oracle Analytics Cloud is lacking in charts. They should add more charts to it."
"It's not a failure of the product; it's just an architectural choice. It has to do with data modeling. I'm comparing this to another product, which is Oracle's developer client and probably called Oracle BI Developer Client Tool. The data modeler, which is cloud-based, and Oracle BI Developer Client Tool, which is local or on-premises-based, both can do the same thing in data modeling. However, the cloud tool does not have as many features as the Oracle BI Developer Client Tool, which is closest to the OBIEE Administration Tool with full feature data modeling, metadata development, and so forth. In a complex environment or implementation, that is the capability that you need."
"It is less scalable than Snowflake."
"Analytics Cloud allows you to merge various data types and structure data from multiple sources."
"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."
"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."
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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