Deploying Sisense on Amazon provides a flexible and powerful hybrid solution for moving all your data to and from the cloud seamlessly.
Sisense provides an end-to-end, fully functional business analytics platform that gives users everything they need to analyze data in one single, intuitive tool: ETL and data management, basic and advanced analytics features, and a stunning visualization and dashboard reporting interface. We’ve partnered with Amazon (AWS BI) to give our users an unparalleled cloud BI solution, allowing you to move all your data to the cloud quickly, hassle-free and with minimal implementation costs.
The Sisense data engine is built on award-winning home-grown technology that is designed to crunch large datasets coming from multiple sources and provide faster, more detailed results:
Storing your data on Amazon (AWS BI) means you only pay for the storage you actually use, allowing you to easily scale when your needs grow. Replace the expensive initial investment usually associated with BI projects with a predictable subscription that includes everything you need to get started. Purchase more capacity or replicate environments to support additional users in a click, and on your own timeline.
Sisense provides the same service on-premises and in the cloud. Utilizing Amazon’s virtual machine features makes it simpler than ever to move your data to and from the cloud and allows you to enjoy the best of both worlds., Sisense does this with
minimal disruption to users’ budgets or patience.
Cloud storage means your data is accessible anywhere and at any time. Once the data is uploaded to Amazon, you can easily connect Sisense to it and to additional cloud or on-premises sources and start analyzing and visualizing your data. Web-based dashboards drive collaboration between teams, while Sisense technology ensures performance stays lightning-fast even with dozens of concurrent users. Build customer-facing dashboards and display them in the cloud, without directing this external
traffic to your own servers and data.