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/ 5 MIN READ

Using AtScale’s Semantic Layer with Data Science Use Cases

AtScale’s semantic layer allows users to have one single place to define business constructs like KPIs (i.e. time series calculations) and first-class dimensionality/hierarchies (i.e. time, geography, product, customer, etc.). Whether you are on the business…

Posted By: Daniel Gray

/ 5 MIN READ

How to Turn Unstructured Medical Records Into Actionable Insights

Patient documentation has come a long way since the days of doctors writing patient notes with pen and paper. Today, Electronic Medical Record (EMR) systems can capture, exchange, and store medical records automatically. These advances…

Posted By: Dave Mariani

/ 5 MIN READ

A Business-Oriented Semantic Layer for Your Databricks Lakehouse

A semantic layer strategy lays the foundation for a scalable business intelligence and enterprise AI program and complements the power of modern cloud data platforms.  Key benefits include: Business metrics stay consistent across the organization. …

Posted By: Anurag Singh

/ 4 MIN READ

How A Semantic Layer simplifies Your Data Architecture

*This post was originally published by the author, Anurag Singh. You can view the original post here . Making data accessible to everyone within an organization is a challenge that most companies face. For example,…

Posted By: Anurag Singh

/ 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

/ 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

/ 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

/ 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

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