September 14, 2021
Building Time Series Analysis on Snowflake with a Semantic LayerIn a recent post, we discussed how a semantic layer helps scale data science and enterprise AI programs. With massive adoption of Snowflake’s cloud data platform, many organizations are shifting analytics and data science workloads to the Snowflake cloud. Leveraging the…
August 17, 2021
Making Raw Data Analysis-Ready with Dimensional ModelingTurning raw data into analysis-ready data sets for Business Intelligence (BI) and analytics teams is a challenge for many organizations. While collecting and storing information is easier than ever, delivering data sets that are fully prepped for analysts and decision…
August 12, 2021
Building a Semantic Layer with AtScale on Amazon RedshiftUsing AtScale to establish a semantic layer on Amazon Redshift delivers several important benefits to modern data and analytics teams. As a single source of governed metrics, and dimensions, AtScale extends the value of Redshift for business intelligence and data…
August 10, 2021
Breaking the Cognitive Bottleneck with Prescriptive AnalyticsModern organizations increasingly rely on their analytics programs to help them stay competitive. And, while most every organization is leveraging the massive amounts of data available from their enterprise applications and from 3rd party data providers, it is increasingly common…
July 20, 2021
Accessing Analysis-Ready Third-Party Data with a Semantic LayerIn a previous post, we talked about using AtScale’s semantic layer to merge Foursquare Places data with first-party data. By blending third-and first-party data, organizations can improve their decision-making capabilities using advanced analytics and predictive data modeling. In this post,…
July 15, 2021
How a Semantic Layer Improves Tableau Performance for Dimensional AnalysisI have always been a fan of Tableau, and we have written before on the powerful combination of Tableau and AtScale. With Tableau expanding on its BI and Analytics position with their Salesforce.com acquisition, the potential for building powerful BI…
July 8, 2021
5 Benefits of a Semantic Layer in a Data Fabric DesignIn the first post of this series on Data Fabrics, we defined the enterprise data fabric design pattern and how it can transform your data and analytics operations into a self managing, data factory. And, in our second piece, we…
July 7, 2021
The Role of the Semantic Layer in a Data Fabric DesignIn our first post of this series, we explored the notion of a Data Fabric as a design pattern for assembling technologies and processes to support modern data and analytics infrastructure. Now that we better understand what data fabric is…
June 16, 2021
How Insurance Companies Can Merge Foursquare Places Data with First-Party DataMany insurance companies are looking to tap the power of third-party data to gain critical insights into their potential customer base. By blending third-party data with first-party data, these insurance providers can improve their decision-making capabilities through advanced analytics and…
June 10, 2021
AtScale in Action: Scaling Self-Serve BI Program on Snowflake With a Semantic LayerThe ability to access data from Snowflake in a consistent manner is crucial for scaling analytics programs and building a self-service BI culture. In addition to providing control and analytics governance, the AtScale universal semantic layer accelerates BI query performance…
