August 26, 2021

Leveraging Calculated Measures in AtScale for Time Series Analysis

AtScale can help BI users and data scientists operate more efficiently by getting more from their semantic layer solution to support sophisticated analyses like predictions, forecasting, and analyzing pattern anomalies as examples. In this post, we’ll discuss how to leverage…

Posted by: Dave Mariani

August 17, 2021

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 prepped for analysts and decision…

Posted by: Dave Mariani

August 12, 2021

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 for business intelligence and data…

Posted by: Dave Mariani

August 10, 2021

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 providers, it is increasingly common…

Posted by: Dave Mariani

August 5, 2021

AtScale in Action: How to Make Power BI Perform on Snowflake

Many enterprises today choose the Microsoft stack because it fits seamlessly with the Windows OS and existing business applications. That’s why AtScale has partnered with Snowflake to streamline reporting and analytics with Power BI. If you haven’t seen our previous…

Posted by: Dave Mariani

July 29, 2021

User Story: The Journey to Self-Service Data Analytics

Self-service data analytics is a major milestone for many enterprises, but it often requires an iterative approach to data and analytics architectures to get there. Learn more about how a multi-billion dollar consumer packaged goods leader built a world-class self-service…

Posted by: Dave Mariani

July 27, 2021

Augmented Analytics: The Convergence of BI and Data Science

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…

Posted by: Daniel Gray

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