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/ 8 MIN READ

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…

Posted By: Dave Mariani

/ 7 MIN READ

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…

Posted By: Dave Mariani

/ 9 MIN READ

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.…

Posted By: Dave Mariani

/ 4 MIN READ

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…

Posted By: Daniel Gray

/ 5 MIN READ

Breaking the Cognitive Bottleneck with Prescriptive Analytics

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…

Posted By: Dave Mariani

/ 5 MIN READ

Building a Semantic Layer with AtScale on Amazon Redshift

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…

Posted By: Dave Mariani

/ 5 MIN READ

Making Raw Data Analysis-Ready with Dimensional Modeling

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…

Posted By: Dave Mariani

/ 8 MIN READ

Building Time Series Analysis on Snowflake with a Semantic Layer

In 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…

Posted By: Daniel Gray

/ 7 MIN READ

How a Semantic Layer Turns Excel into a Sophisticated BI Platform

Microsoft Excel has been the workhorse analytics tool for generations of business analysts, financial modelers, and data hacks. It delivers the ultimate flexibility to manipulate data, create new metrics with cell calculations, build live visualizations…

Posted By: Josh Epstein