We performed a comparison between Google Cloud Datalab and Sisense 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."In MLOps, when we are designing the data pipeline, the designing of the data pipeline is easy in Google Cloud."
"Google Cloud Datalab is very customizable."
"All of the features of this product are quite good."
"The infrastructure is highly reliable and efficient, contributing to a positive experience."
"The APIs are valuable."
"Whenever we have an issue or are unsure how to proceed, they manage to simplify the issue and help us execute it in a graceful and scalable way."
"Visually displaying data."
"The solution's technical support team is good."
"The dashboard design interface is very intuitive and allows you to quickly and easily produce professional, innovative dashboards."
"Ability to work with very large data sources without the limitations of Excel."
"This solution is easy to learn how to use."
"There are many built-in connectors, which allow us to easily add new sources of data, often in minutes."
"No issues with stability. It is a very stable solution."
"Connectivity challenges for end-users, particularly when loading data, environments, and libraries, need to be addressed for an enhanced user experience."
"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."
"The interface should be more user-friendly."
"The product must be made 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."
"At present there are additional costs involved if we wish to share our data queues within this solution, which we would like to see removed."
"I would like to see more development and growth for the support of Knowledge Base and Community forums."
"I would love to have more customization capabilities for building dashboards, especially in creating custom widget sizes."
"Larger datasets will sometimes give a "Accumulated logs" error when trying to make minor changes. T"
"The initial version we purchased only ran on Windows servers, which was less than ideal for our DevOps team. I believe that has been remedied in the latest release."
"The solution's setup process could be easier."
"I would like Sisense to improve its performance, particularly when we are dealing with large-scale data."
"The administrative side of Sisense is a little cumbersome and confusing."
Google Cloud Datalab is ranked 20th in Data Visualization with 5 reviews while Sisense is ranked 14th in Data Visualization with 39 reviews. Google Cloud Datalab is rated 7.6, while Sisense is rated 8.8. 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 Sisense writes "Business intelligence solution that has improved automation and provided meaningful insights". Google Cloud Datalab is most compared with Databricks, IBM SPSS Statistics, Cloudera Data Science Workbench, KNIME and Qlik Sense, whereas Sisense is most compared with Microsoft Power BI, Tableau, Apache Superset, Qlik Sense and Amazon QuickSight. See our Google Cloud Datalab vs. Sisense report.
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We monitor all Data Visualization 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.
