February 1, 2022
A Business-Oriented Semantic Layer for Your Databricks LakehouseA 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. Analysts can access a broader…
January 21, 2022
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, data scientists generate forecasts and predictions…
December 14, 2021
Data Insights for Everyone — The Semantic Layer to the Rescue*Author’s Note This article was originally posted as a LinkedIn article by Kirk Borne. Kirk Borne is the Chief Science Officer at Data Prime and has been an influential globally recognized leader in the data science space for 20 years.…
October 28, 2021
The Universal Semantic Layer. More Important than Ever.There’s been a lot of news lately about semantic layers. Google and Tableau announced their plans to connect Tableau to Looker’s semantic layer. It’s great to see the industry recognize the importance of the semantic layer in the new cloud…
September 22, 2021
How a Semantic Layer Turns Excel into a Sophisticated BI PlatformMicrosoft 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 and slice and dice data.…
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,…
