July 20, 2021

Accessing Analysis-Ready Third-Party Data with a Semantic Layer

In 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,…

Posted by: Daniel Gray

July 13, 2021

How AtScale Uses Aggregates to Optimize Query Performance

The use of data aggregations (i.e. aggregates) to accelerate query performance is a common practice for data engineering teams, but the question remains how to balance resources like time and compute consumption in the aggregation process. Rather than relying on…

Posted by: Dave Mariani

July 8, 2021

5 Benefits of a Semantic Layer in a Data Fabric Design

In 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…

Posted by: Dave Mariani

July 7, 2021

The Role of the Semantic Layer in a Data Fabric Design

In 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…

Posted by: Dave Mariani

June 22, 2021

Analytics Query Acceleration in the Age of Cloud Data Platforms

Late last year, Gartner published their first Market Guide for Analytics Query Accelerators (available with Gartner Subscription).  They loosely define this broad set of technologies as providing “[query] optimization on top of semantically flexible data stores, typically associated with data…

Posted by: Dave Mariani

June 16, 2021

How Insurance Companies Can Merge Foursquare Places Data with First-Party Data

Many 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…

Posted by: Daniel Gray

June 4, 2021

How Data Virtualization Supports BI & Analytics

Data virtualization refers to a general technology approach of abstracting data away from physical data sources, including data warehouses, data lakes, application data, without having to copy or move it. Data virtualization solutions are generally grouped in with other data…

Posted by: Dave Mariani

June 1, 2021

How to Use a Semantic Layer for Data and Analytics

You may have heard the term semantic layer before, as it’s been around for some time. Semantic layers were invented to mold relational databases and their SQL dialects into an approachable interface for business users. Until recently, however, the semantic…

Posted by: Chris Oshiro