Current enterprise data architectures include NoSQL databases co-existing with RDBMS. In this article, author discusses a solution for managing NoSQL & relational data using unified data modeling.

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.

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.

Announced January 17, Toad Data Studio allows users to manage nearly any database platform in their environment including cloud and on-premises sources and relational, NoSQL, and data warehouse ...

More specifically, let’s look at data warehouses, data marts, operational data stores, data lakes, and their differences and similarities. A data warehouse was first formally defined by Bill Inmon in ...

Data warehouse software stores data in its raw form, while BI software displays the data in an easy-to-understand format and provides insights based on the information. Therefore, data warehouse ...

ERP apps and data warehouses/data marts, however, are best suited for relational SQL databases. McCauley then detailed DynamoDB tables and their specific advantages for legacy relational migration, ...

Data warehouses, which are themselves relational databases, can be complex to set up and manage on a daily basis, so they typically require significant human involvement from database ...

Requirements by the U.S. Food and Drug Administration for record-keeping and documentation underscore the need for data warehousing. Compliance requirements in regulations ranging from 21 CFR Part ...