Since Snowflake announced general availability on Azure in November 2018, increasing numbers of customers are deploying their Snowflake accounts on Azure, and with this, more customers are using Power BI as the data visualization and analysis layer. As a result of these trends, customers want to understand the best practices for a successful deployment of Power BI with Snowflake.
This is the second post in a series about data modeling and data governance in the cloud from Snowflake’s partners at erwin. See the first post here. As you move data from legacy systems to a cloud data platform, you need to ensure the quality and overall governance of that data. Until recently, data governance was primarily an IT role that involved cataloging data elements to support search and discovery.
In August 2020, Snowflake announced several new features, all in preview, that make its cloud data platform easier to use, more powerful for sharing data, and more usable via Snowflake-supported languages. These innovations mean you can bring more workloads, more users, and more data to Snowflake, helping your organization solve your most demanding analytics challenges. Multi-Cloud, Cross-Cloud, and Pattern-Matching Support in Snowpipe
At Snowflake, our number one company value is “put customers first. We only succeed when our customers do. And how we help enable their success depends on how well we serve them as a technology provider. To understand if our efforts meet their needs, we conduct an annual Customer Experience Relational Survey. As we’ve done each year, we are pleased to share the findings of this year’s survey, conducted in May 2020 and produced in partnership with Walker.
