Data teams are faced with the challenge of transforming raw data into analysis that is accurate and comprehensible. Ideally, the data modeling work they do will make it easy to answer crucial questions across multiple teams, and can be shared for collaboration without having to reinvent the wheel every time a new question pops up. 



However, the process of turning data into insights is not linear. It often requires learning and maintaining multiple different tools to manage the data pipeline for consistent and reusable analysis. With the pressures of getting insights fast to stay competitive, data teams need a single, low maintenance tool that will accelerate this workflow.&nbsp;&nbsp;



<h4 class="wp-block-heading">Introducing Data Engine, now compatible with your own warehouse</h4>



<figure class="wp-block-image size-large fancybox"><img decoding="async" src="https://cdn.sisense.com/wp-content/uploads/pd-one-pager-architecture-20191106-01-bl1-770x403.jpg" alt="" class="wp-image-87241"/></figure>



With Data Engine, teams get visibility and control over their entire data pipeline, whether it’s on your own Redshift or Snowflake warehouse or on Sisense by Cloud Data Teams-managed infrastructure. The latest release provides the ability to materialize views directly back to your warehouse with full control over how often those views refresh. This means that teams can achieve faster performance while better managing warehouse utilization and data freshness in the same platform that powers the rest of the BI workflow. And with the advanced modeling capabilities available in Sisense for Cloud Data Teams, teams can use SQL, Python, and R to perform complex analysis, then store the results to streamline future analytics projects without having to switch tools. 



Data Engine on your warehouse empowers data teams to:&nbsp;



<ul><li>Optimize query performance — reduce query run times from hours to seconds by materializing views directly into a Redshift or Snowflake warehouse</li><li>Efficiently manage warehouse resources — better manage compute costs by materializing and scheduling views that require long run times or have high complexity</li><li>Control data freshness — fully control data freshness with flexible view scheduling options</li></ul>



If you’re already a Sisense customer and you’re interested in learning more about Data Engine, contact your customer success manager. If your company isn’t a customer, the best way to see these features in action is to try them for yourself.

Sisense for Cloud Data Teams Summer Release 2019: Accelerate Your Analytics Workflow with Data Engine

LinkedIn

Twitter

GitHub

curve-image-unique-image-unique

curve

3-dark-2-image-unique-image-unique

3 DARK 2

Get the latest in analytics right in your inbox.

Article