Big data systems, whether on-premises or in the cloud, need to combine with the power of online analytical processing (OLAP) technology to make it possible to design powerful inquiries across hundreds of billions of records and myriad dimensions. With traditional OLAP solutions, however, it is both time-consuming and expensive to ingest data, transform it, and prepare it for analysis. Also, because data is increasingly stored across heterogenous, geographically dispersed data sources, including on-premises systems and cloud-based repositories, data analysts are spending more of their time integrating data using extract, transform, and load (ETL) processes rather than analyzing it. Organizations need solutions that flip this ratio around. Read More!