The cubes Kyvos can build and run on Hadoop are orders of magnitude bigger than what could be built on traditional OLAP gear. Instead of getting rid of the granular level of detail that would ...

New version 4 of the BI + big data platform is based on Spark and Parquet, leverages machine learning and is optimized for cloud storage. It's available on the Azure Marketplace now and coming to ...

With virtual cubes, the changes are instantaneous and are ready for analysis in real-time. My take One final thought about the need for OLAP: all of the data science in the world can’t match the ...

An OLAP Cube (source: Konrad Roeder derivative work on Wikipedia) Now, Kyvos is promising the same, bringing OLAP analysis from the exlusive province of the traditional data warehouse, where various ...

Conversely, OLAP solutions suffer from having to constantly rebuild or add on to the existing cube (s) to remain current. Most cubes contain far more data than the users ever look at.

But data cubes aren’t restricted to just three dimensions. Most online analytical processing (OLAP) systems can build data cubes with many more dimensions—Microsoft SQL Server 2000 Analysis ...

Octopai’s expanded coverage of the OLAP Cube, a multi-dimensional array of data, allows business intelligence users to easily view reports and perform speedy data analysis across their entire data ...

A OLAP cube is created by extracting data from multiple sources and storing them in data warehouses, from which they are cleaned and then stored in OLAP cubes. Users get data from these cubes by ...

The OLAP cube approach is not new, and has been used in the business intelligence world for decades. By storing the data in a cube format, it essentially is pre-aggregated along commonly used ...

Cubes used for OLAP allow fast data analysis by defining dimensions that define the problem being analysed. It might, for example, consist of 7 Dimensions: Salesperson, Sales Amount, Region, Product, ...