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ASIN ‏ : ‎ B00QQ6B8X0
Publisher ‏ : ‎ Morgan Kaufmann; 1st edition (November 26, 2014)
Publication date ‏ : ‎ November 26, 2014
Language ‏ : ‎ English
File size ‏ : ‎ 23860 KB
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 343 pages
Page numbers source ISBN ‏ : ‎ 012802044X

Data architecture is a critical component of any data scientist’s toolkit, as it provides the foundation for organizing and managing data in a way that supports analytics and decision-making. In this primer, we will explore three key concepts in data architecture: Big Data, Data Warehouse and Data Vault.

Big Data is a term that refers to the massive volumes of structured and unstructured data that organizations collect and analyze to gain insights and make informed decisions. Big Data technologies such as Hadoop and Spark are used to store, process and analyze this data, enabling organizations to uncover patterns, trends and correlations that were previously hidden.

A Data Warehouse is a centralized repository of structured data from various sources within an organization. It is designed to support reporting and analysis, providing a single source of truth for decision-makers. Data Warehouses often use a dimensional modeling approach, organizing data into facts (measurements) and dimensions (descriptive attributes), making it easier to query and analyze data.

Data Vault is a data modeling technique that focuses on flexibility, scalability and agility in data architecture. It is designed to accommodate changing business requirements and evolving data sources, making it easier to adapt to new data sources and analytical needs. Data Vault models consist of three types of tables: Hubs (business entities), Links (relationships between entities) and Satellites (historical data attributes).

By understanding and utilizing these key concepts in data architecture, data scientists can design and implement robust data pipelines that support their analytical needs. Whether working with Big Data, Data Warehouses or Data Vaults, having a solid foundation in data architecture is essential for success in the field of data science.
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