Scale up and modernize your OLAP infrastructure while keeping SSAS’
strong multidimensional functionality and live connection to Excel.
Overcome the limitations of working with just pre-aggregated, incomplete and stale data. Improve your data visibility.
Improve your time-to-insights by eliminating the need for in-house OLAP and data modeling expertise.
Work with your data without the need for ETL. Skip the cost and complexity of data movement.
Analyze data in an open-ended, exploratory method with distributed Big Data.
Handle any volume of data without the need to pre-calculate a cube with a live connection. AtScale pushes work down to the data platform (i.e. database, data lake) so you don’t have to scale AtScale separately from your data warehouse. AtScale’s acceleration structures improve performance because new data is automatically added incrementally and raw data is accessible at any time.
AtScale creates a virtual cube so new dimensions or measures can be added in a few minutes without the need to rebuild the cube.
AtScale re-invented cube authoring with an easy to use web interface that allows a BI user (not an OLAP expert) to create new virtual cubes from the top down without requiring them to be an expert in OLAP.
AtScale’s Design Center web based cube authoring tool is built for collaboration and reuse because it supports multiple users editing the same cube and has a library of dimensions and calculations that can be reused across any number of cubes.
Mitigate the challenges for current intelligence modeling including out of IT control, inconsistent table joins, inconsistent security and calculations and unnecessary duplication of effort.
Leverage existing data and it available across the organization so that analysts can get a more complete view into data for BI initiatives.
Make data available to more employees without re-engineering data or forcing a company-wide standard.
Provide access to existing data to more data analysts while keeping data secure and properly governed.
Leverage existing architecture to get live connections to data for analysis.
Rakuten invested in a data-driven business environment and customer experience by consolidating their data into an on-premise data lake using Hadoop coupled with AtScale’s virtual OLAP cubes to analyze their data. After initial success, the Hadoop cluster’s physical limitations began to cause painful business disruptions as disk and processor usage hit critical levels. Rakuten needed a new data warehousing solution that would ensure responsive data analysis during peak demand periods.
Rakuten would restructure their data infrastructure by moving from their on-premise Hadoop cluster to a Snowflake cloud data warehouse on AWS. Colder, less frequently used data would reside in S3 on AWS, while hotter, high-demand data would reside on Snowflake. AtScale would provide query optimization and semantic connections to the data.
Fortune 50 DIY retailer optimizes a cloud data platform to increase ROI per analysis.
Toyota leverages AtScale, accelerating time-to-insight from weeks to minutes.
Fortune 100 industrial conglomerate embraces the cloud without business disruption.
Learn how the prevalence of cloud transformation as a critical enterprise initiative has led to the emergence of the adaptive analytics fabric with intelligent data virtualization as a new paradigm in enterprise data architecture.
DOWNLOAD NOWDo you love the OLAP functionality of SSAS but hate its complexity, scale limitations and long cube build times? Check out how AtScale allows you to scale up and modernize your OLAP infrastructure and keep multidimensional functionality.
DOWNLOAD NOWSee a demonstration of how AtScale helps organizations scale and modernize their OLAP infrastructure. Learn how to migrate your SSAS cubes to a modern, cloud data warehouse and scale OLAP to a 100TB data warehouse with 300 billion rows of data, without a cube build.
WATCH NOWDeliver data for business intelligence and machine learning analytics just-in-time.
Shift resources from managing distributed data silos—to analyzing them.
