While the architecture of a data warehouse can vary according to different organizational needs, most enterprises tend to follow a three-tier system with a bottom, middle and top layer.

This article was contributed by Gunasekaran S., director of data engineering at Sigmoid. Over the years, cloud data lake and warehousing architectures have helped enterprises scale their data ...

Another camp follows the advice of Ralph Kimball, another prolific author and respected industry figure. The Kimball model dismisses the need for a data warehouse. Because most users want detailed ...

Introduction to Data Vault Architecture Data Vault 2.0. Architecture Data Vault 2.0 Architecture is based on three-tier data warehouse architecture. The tiers are commonly identified as staging or ...

Atlassian was always an advocate of the data warehouse-style architecture, according to the company's data platform senior manager Rohan Dhupelia. At one point the company was running two data ...

Organizations that really want to take advantage of a higher performance, more agile and lower cost data warehouse architecture, should implement master data management (MDM) to improve data quality.

In fact, more IBM customers are moving toward a “logical data warehouse” architecture in which relational platforms are increasingly supplemented, but not supplanted, by Hadoop platforms.

Harmonize data lake and data warehouse architecture to drive efficiency and optimization. Apply Gartner’s decision framework to map use cases to data storage options.

Formed back in 2014 with first product released three years later, Yellowbrick has specialized at the high-performance end of the data warehousing spectrum that includes analytics of real-time ...