September 13, 2021
10 Ways AtScale Helps Organizations Scale Data Science and Enterprise AIAtScale has been helping bridge Enterprise BI and Data Science for years, recently announcing AtScale AI-Link to simplify access to our semantic layer platform with a Python library designed for data scientists. We clearly see an explosion of interest around…
September 9, 2021
How Analytics Governance Empowers Self-Service BIData governance is a broad topic with a lot of players offering commentary and strategy across the data and analytics space. Governance isn’t only about security and access control, or who can access what; it’s also about how data is…
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 27, 2021
Augmented Analytics: The Convergence of BI and Data ScienceOne 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…
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,…
July 15, 2021
How a Semantic Layer Improves Tableau Performance for Dimensional AnalysisI 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…
July 13, 2021
How AtScale Uses Aggregates to Optimize Query PerformanceThe 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…
July 8, 2021
5 Benefits of a Semantic Layer in a Data Fabric DesignIn 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…
