The exponentially increasing amounts of data being generated each year make getting useful information from that data more and more critical. The information frequently is stored in a data warehouse, ...

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Amilcar has 10 years of FinTech, blockchain, ...

Excerpt from Microsoft SQL Server 2008 Analysis Services Unleashed by Irina Gorbach, Alexander Berger, Edward Melomed Chapter 1: Introduction to OLAP and Its Role in Business Intelligence Excerpt from ...

The digitization of the modern business enterprise has created a seemingly never-ending stream of raw data. Gleaning actionable nuggets of information from terabytes upon terabytes of data requires ...

Data warehouse systems have been at the center of many big data initiatives going as far back as the 1980s. Today companies from leading cloud hyperscalers such as Amazon Web Services (Redshift) and ...

The true measure of an effective data warehouse is how much key business stakeholders trust the data that is stored within. To achieve certain levels of data trustworthiness, data quality strategies ...

Enterprise data warehouses, or EDWs, are unified databases for all historical data across an enterprise, optimized for analytics. These days, organizations implementing data warehouses often consider ...

Essentially, a data warehouse is an analytic database, usually relational, that is created from two or more data sources, typically to store historical data, which may have a scale of petabytes. Data ...

Data lakes are cool, but you don’t have to jump in head-first. It’s easy to start by dipping a toe: Integrating a legacy data warehouse into a data lake leverages the structured systems that have been ...