From basic information storage mechanisms, data warehouse solution has evolved to play an integral role in insight generation and data-driven decision-making.

There once was no alternative to building a data warehouse on-premise, but with cloud providers targeting analytics, building data warehouses on-site is looking obsolete.

Data quality is paramount in data warehouses, but data quality practices are often overlooked during the development process.

VP & Principal Analyst, Datacenter Compute, Matt Kimball, covers the recent updates to Oracle's Autonomous Data Warehouse (ADW) service, providing his thoughts on how these advancements position ...

Also read: Top Big Data Storage Products Differences between data lake and data warehouse When storing big data, data lakes and data warehouses have different features. Data warehouses store ...

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

Automation can accelerate all stages of data management and data warehousing, including data collection, integration, preparation, storage, sharing, and analysis. It can even speed up the ...

Topics discussed that support the Data Warehousing process are data modeling, data transformation, multi-dimensional databases, data extraction and storage, warehouse loading, client/server, and the ...

These are Teradata data warehouse systems: massive, high-performance storage systems sold to enable: . . . the analytical performance you need . . . to intelligently process all types of ...

First, there was a data warehouse – an information storage architecture that allowed structured data to be archived for specific business intelligence purposes and reporting. The concept of the ...