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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
Author Profile Icon Bas Geerdink
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

Introduction to File Formats

Now, let's understand the file structure in detail and distinguish between these file formats. This section will decompose the file formats and dive into the structure of files to elaborate on the efficiency of each file format.

Parquet

Apache Parquet is an open-source column-oriented representation and stores data in an optimized columnar format. It is language-independent and framework-independent because the objective of creating this format was to optimize the operation and storage of data across Hadoop.

Shortly after its introduction, it acquired popularity in the industry. The reasons for its acceptance are primarily the fast retrieval and processing capabilities that it offers. However, writes are usually time-consuming and considerably expensive.

As it is a columnar-based format, homogenous data is stored together, resulting in better compression. The compression and encoding scheme can have a significant impact on performance.

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