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

Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist.

Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to:

Turn textual information into a form that can be analyzed by standard tools.Make the connection between analytics and Big DataUnderstand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data

Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using itShows how to turn textual information into a form that can be analyzed by standard tools.Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data

1. Corporate Data 2. Big Data 3. Data Warehouse 4. Data Vault 5. Operational Systems 6. Architecture 7. Analysis and Visualization of Data 8. Analytics for Structured Data 9. Analytics for Unstructured Repetitive Data 10. Analytics for Unstructured Non-Repetitive Data 11. Glossary of Terms