Using Universal Semantic Layer with Your BI Tools

Dave Mariani, Chief Technology Officer at AtScale, explains the big differences between a DIY approach and using the Universal Semantic Layer approach to integrate your BI tools. In this demo, he demonstrates the technical details, such as:
- How using DIY Power BI tools in your window environment leads to a longer and more convoluted process, requiring creating new data stores, more data movement, and the need to scale down what information you’re able to view.
- How data and analytics leaders are able to support the demands of business stakeholders using AtScale’s business intelligence platform.
- How using a universal semantic layer to virtualize data leads to improved innovation cycles across data consumption use cases.
- How data-driven organizations can increase what’s possible with their existing analytics and business intelligence (BI) stack.
Take your company to the next level and make smarter, data-driven decisions with AtScale.
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[@thinkof2morrow]

Nice Demo. Made perfect sense. I have been telling my company for years we needed better cube generators between the data systems (like Snowflake) and our BI tools. This is cubes on steroids. Nicely done.

[@severtone263]

Great demo. The 480 res is not helping tho Mr Average.

[@AnishMahapatra01]

The video quality should be 1080p at minimum,

[@PAWANKUMAR1491]

Video should be at least in 720p.

[@darrensiegert5333]

The concept of AtScale is great. As AtScale is using the MS Analysis Services connection in Excel, does this means you can use Excel CUBESET functions over an AtScale universal Semantic model?

[@radekou]

I've been following your posts with interests, it seems like this long existing gap in functionality within the "modern" data stack looks to be filled. So in this example, is it Snowflake running the queries? Does AtScale work out which queries to run against Snowflake similar to how Power BI (SSAS) Works out what queries to run against the Source? Lastly: I think the cartesian product claim: have you actually looked what queries were issues against the source? Seemed to run way too fast for a cartesian product.

[@lakshmilfg1147]

Open source SAP BO Universe

[@Milhouse77BS]

Pretty crappy demo of Power BI. It's why you use Import instead of DirectQuery when possible. and you use the "semantic model" in Power BI to publish data to end users and analysts via Power BI datasets instead of raw tables.