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The Artificial Intelligence Infrastructure Workshop

You're reading from   The Artificial Intelligence Infrastructure Workshop Build your own highly scalable and robust data storage systems that can support a variety of cutting-edge AI applications

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Product type Paperback
Published in Aug 2020
Publisher Packt
ISBN-13 9781800209848
Length 732 pages
Edition 1st Edition
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Authors (6):
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Bas Geerdink Bas Geerdink
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Bas Geerdink
Chinmay Arankalle Chinmay Arankalle
Author Profile Icon Chinmay Arankalle
Chinmay Arankalle
Kunal Gera Kunal Gera
Author Profile Icon Kunal Gera
Kunal Gera
Kevin Liao Kevin Liao
Author Profile Icon Kevin Liao
Kevin Liao
Gareth Dwyer Gareth Dwyer
Author Profile Icon Gareth Dwyer
Gareth Dwyer
Anand N.S. Anand N.S.
Author Profile Icon Anand N.S.
Anand N.S.
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Table of Contents (14) Chapters Close

Preface
1. Data Storage Fundamentals 2. Artificial Intelligence Storage Requirements FREE CHAPTER 3. Data Preparation 4. The Ethics of AI Data Storage 5. Data Stores: SQL and NoSQL Databases 6. Big Data File Formats 7. Introduction to Analytics Engine (Spark) for Big Data 8. Data System Design Examples 9. Workflow Management for AI 10. Introduction to Data Storage on Cloud Services (AWS) 11. Building an Artificial Intelligence Algorithm 12. Productionizing Your AI Applications Appendix

Cassandra

The topic of NoSQL isn't complete without discussing columnar storage. In Cassandra, the data is stored and read in columns instead of rows. Each column is stored separately with the same row offset related to the table. We will study offset in the Data Modeling subsection:

Figure 5.50: Keyspace and column family internal structure

Cassandra is a mixture of structured data, consistency, high scalability, and no single point of failure, and also has a powerful column family design. This particular database was developed by Facebook in 2008 and is horizontally scalable. Imagine you run a multinational company. You have thousands of employees, but you want the information of all the employees at the London location. In such a scenario, a traditional relational database will scan each row, parse it, and compare the u_location column value against the query. This is extremely time-consuming.

On the other hand, the columnar store only requires columns...

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