Data Architecture: A Primer for the Data Scientist: Big Data, Data Warehouse and Data Vault

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Publisher ‏ : ‎ Morgan Kaufmann; 1st edition (December 10, 2014)
Language ‏ : ‎ English
Paperback ‏ : ‎ 378 pages
ISBN-10 ‏ : ‎ 012802044X
ISBN-13 ‏ : ‎ 978-0128020449
Item Weight ‏ : ‎ 1.65 pounds
Dimensions ‏ : ‎ 7.48 x 0.86 x 9.23 inches

Data architecture is a crucial aspect of any data scientist’s work, as it provides the framework for organizing and storing data in a way that is efficient and accessible. In this primer, we will explore three key components of data architecture: Big Data, Data Warehouse, and Data Vault.

Big Data is a term that refers to the massive amounts of data that are generated and collected by organizations on a daily basis. This data can come from a variety of sources, such as social media, sensors, and other digital platforms. Big Data requires specialized tools and techniques for storage and analysis, as traditional databases are often unable to handle the volume and variety of data involved.

A Data Warehouse is a centralized repository of data that is used for reporting and analysis. It is designed to store structured data from various sources in a way that is optimized for querying and analysis. Data warehouses are typically used by organizations to support business intelligence and decision-making processes.

Data Vault is a data modeling technique that is used to design and organize data structures in a way that is flexible and scalable. Data Vault models are made up of three key components: hubs, links, and satellites. Hubs represent business entities, links represent relationships between entities, and satellites contain additional attributes and metadata.

By understanding and implementing these key components of data architecture, data scientists can effectively store, organize, and analyze data to derive valuable insights and support decision-making processes within their organizations.
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