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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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Toc

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

6. Big Data File Formats

Activity 6.01: Selecting an Appropriate Big Data File Format for Game Logs

Solution

  1. In the Chapter06 directory, create the Activity06.01 directory to store the files for this activity.
  2. Move the session_log file into the Chapter06/Data directory.
  3. Open your Terminal (macOS or Linux) or Command Prompt window (Windows), move to the installation directory, and open the Spark shell in it using the following command:
    spark-shell --packages org.apache.spark:spark-avro_2.11:2.4.5

    You should get the following output:

    Figure 6.27: Spark shell

    By using this command, the Spark shell will be launched and we will now load the dataset from the CSV file.

  4. Load the session_log.csv dataset:
    val df_ses_log_csv = spark.read.options(Map("inferSchema"-  >"true","delimiter"->",","header"-  >"true")).csv("F:/Chapter06/Data/session_log.csv")

    Note

    Update the input path of the file according...

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