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 ...

Online analytical processing (OLAP) databases are purpose-built for handling analytical queries. Analytical queries run on online transaction-processing (OLTP) databases often take a long time to ...

According to Gartner, Inc., CIOs need to familiarize themselves with nine key trends in data warehousing and how they will impact the cost-benefit balance of technology deployed to deliver business ...

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 ...

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 ...

The "data" part of the terms "data lake," "data warehouse," and "database" is easy enough to understand. Data are everywhere, and the bits need to be kept somewhere. But should they be stored in a ...

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 ...

Today’s “big data stack” includes databases, data management and integration software, and data analytics tools—all critical components of an effective operational or analytical data system. But all ...