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Apache Spark Machine Learning Blueprints

You're reading from   Apache Spark Machine Learning Blueprints Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Product type Paperback
Published in May 2016
Publisher Packt
ISBN-13 9781785880391
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Alex Liu Alex Liu
Author Profile Icon Alex Liu
Alex Liu
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Table of Contents (13) Chapters Close

Preface 1. Spark for Machine Learning FREE CHAPTER 2. Data Preparation for Spark ML 3. A Holistic View on Spark 4. Fraud Detection on Spark 5. Risk Scoring on Spark 6. Churn Prediction on Spark 7. Recommendations on Spark 8. Learning Analytics on Spark 9. City Analytics on Spark 10. Learning Telco Data on Spark 11. Modeling Open Data on Spark Index

Dataset joining

In this section, we will cover dataset joining techniques. We will also discuss some of Spark's special features for data joining plus some data joining solutions made easy with Spark.

After this section, we will be able to join data for various machine learning needs.

Dataset joining and its tool – the Spark SQL

In preparing datasets for a machine learning project, we often need to combine data from multiple datasets. For relational tables, the task is to join tables through a primary and foreign key relationship.

Joining two or more datasets together sounds easy, but can be very challenging and time consuming. In SQL, SELECT is the most frequently used command. As an example, the following is a typical SQL code to perform a join:

SELECT column1, column2, …
FROM table1, table2
WHERE table1.joincolumn = table2.joincolumn
AND search_condition(s);

To work with the table joining tasks mentioned before, data scientists and machine learning professionals often utilize...

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