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Learning Spark SQL

You're reading from  Learning Spark SQL

Product type Book
Published in Sep 2017
Publisher Packt
ISBN-13 9781785888359
Pages 452 pages
Edition 1st Edition
Languages
Author (1):
Aurobindo Sarkar Aurobindo Sarkar

Table of Contents (19) Chapters

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Spark SQL 2. Using Spark SQL for Processing Structured and Semistructured Data 3. Using Spark SQL for Data Exploration 4. Using Spark SQL for Data Munging 5. Using Spark SQL in Streaming Applications 6. Using Spark SQL in Machine Learning Applications 7. Using Spark SQL in Graph Applications 8. Using Spark SQL with SparkR 9. Developing Applications with Spark SQL 10. Using Spark SQL in Deep Learning Applications 11. Tuning Spark SQL Components for Performance 12. Spark SQL in Large-Scale Application Architectures

Using Spark with MongoDB (NoSQL database)


In this section, we will use with one of the most popular NoSQL databases - MongoDB. MongoDB is a distributed document database that stores data in JSON-like format. Unlike the rigid schemas in relational databases, the data structure in is a lot more flexible and the stored documents can have arbitrary fields. This flexibility combined with high availability and scalability features make it a good choice for data in many applications. It is also free and open-source software. 

Note

If you do not have MongoDB already installed and available, then you can download it from https://www.mongodb.org/downloads. Follow the installation instructions for your specific OS to install the database.

The New York City schools directory dataset for this example has been taken from the New York City Open Data website and can be downloaded from https://nycplatform.socrata.com/data?browseSearch=&scope=&agency=&cat=education&type=datasets.

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