Blog:
Use Cases

Updated August 17, 2021

Turning 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…

Updated August 12, 2021

Using 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…

Updated August 10, 2021

Modern 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…

Updated July 27, 2021

One of the defining themes of digital transformation is the proliferation of AI-driven insights across all enterprise business processes. With the growth of cloud data platforms, the complexity of managing big data has been radically reduced. The availability of powerful…

Updated July 20, 2021

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,…

Updated July 15, 2021

I 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…

Updated July 13, 2021

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…

Updated July 8, 2021

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…

Updated July 7, 2021

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…

Updated June 22, 2021

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…

Updated June 4, 2026

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…

Updated June 1, 2021

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…