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/ 4 MIN READ

bol.com and Google BigQuery: The Journey, Performance and Cost

In our most recent webinar , Maurice Lacroix, BI Product Owner of bol.com and Dave Mariani, Co-Founder and Chief Strategy Officer of AtScale share how Maurice and his team are able to increase the ROI…

Posted By: Kayla Chiara

/ 2 MIN READ

Introducing AtScale University

Introducing AtScale University As we adjust to the “New Normal” in the midst of the COVID-19 pandemic, going back to school this fall will be anything but ordinary. While some schools have become fully remote…

Posted By: Mahika Saxena

/ 5 MIN READ

How To Reduce Cloud Compute Costs and Boost BI Performance

In our most recent webinar , Mark Stange Tregear, VP Analytics of Rakuten and Dave Mariani, Co-Founder and Chief Strategy Officer of AtScale share five tips to increase the ROI of your Snowflake investment while…

Posted By: Kayla Chiara

/ 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

/ 4 MIN READ

Security & Governance in Action

Security and governance are top of mind in an era where data is everywhere and bad actors are trying to steal it.  But it’s not just hackers we need to worry about. Providing inappropriate data…

Posted By: Dave Mariani

/ 6 MIN READ

Cloud Data Warehouse Modeling: Traditional vs. New

New data model advancements (nested and non-scalar data types) for data lakes and cloud data warehouses are a game changer. However, the existing BI and AI toolsets are really not geared to take advantage of these new innovations. AtScale’s Adaptive Cachet will readily accept your existing star schemas and optimize them for the data denormalization and full table scans these data platforms prefer automatically.

Posted By: Dave Mariani

/ 5 MIN READ

Interactive, Large Volume AI/ML Queries At Scale And Without Data Movement for Self-Service Analytics Empowerment

AtScale’s 2019.2 product release introduces a time-series and time-relative analysis capability for large volumes of data across disparate databases and platforms. This enables data analyst and data science teams to easily access large volumes of time-series data and quickly query and configure data for any (BI), artificial intelligence (AI) or machine learning (ML) tool.

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/ 6 MIN READ

Why AtScale Wins in the Cloud Data Platform Ecosystem

“Never make permanent decisions on temporary feelings”. Technology is risky, and architectural decisions can have a considerable effect on the viability of your business. This post lays out strategies to future-proof your data architecture including rules around virtualization, security, reducing complexity.

Posted By: Matt Baird

/ 5 MIN READ

Real-Time Data: Satisfying the Need for Speed in BI

Business Intelligence (BI) transforms how enterprises make decisions by delivering insights across the entire business. These insights are limited only by the curiosity of the people asking the questions and the speed at which the systems and software that support analytics can perform. BI relies on acceleration and performance (a.k.a. “speed”) to enable data exploration and data mining at a tempo that keeps the human brain engaged.

Posted By: Chuck Smith