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Mastering Geospatial Analysis with Python

You're reading from   Mastering Geospatial Analysis with Python Explore GIS processing and learn to work with GeoDjango, CARTOframes and MapboxGL-Jupyter

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Product type Paperback
Published in Apr 2018
Publisher Packt
ISBN-13 9781788293334
Length 440 pages
Edition 1st Edition
Languages
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Authors (3):
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Silas Toms Silas Toms
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Silas Toms
Paul Crickard Paul Crickard
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Paul Crickard
Eric van Rees Eric van Rees
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Eric van Rees
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Table of Contents (18) Chapters Close

Preface 1. Package Installation and Management 2. Introduction to Geospatial Code Libraries FREE CHAPTER 3. Introduction to Geospatial Databases 4. Data Types, Storage, and Conversion 5. Vector Data Analysis 6. Raster Data Processing 7. Geoprocessing with Geodatabases 8. Automating QGIS Analysis 9. ArcGIS API for Python and ArcGIS Online 10. Geoprocessing with a GPU Database 11. Flask and GeoAlchemy2 12. GeoDjango 13. Geospatial REST API 14. Cloud Geodatabase Analysis and Visualization 15. Automating Cloud Cartography 16. Python Geoprocessing with Hadoop 17. Other Books You May Enjoy

What is Hadoop?

Hadoop is an open-source framework for working with large quantities of data spread across a single computer to thousands of computers. Hadoop is composed of four modules:

  • Hadoop Core
  • Hadoop Distributed File System (HDFS)
  • Yet Another Resource Negotiator (YARN)
  • MapReduce

The Hadoop Core makes up the components needed to run the other three modules. HDFS is a Java-based file system that has been designed to be distributed and is capable of storing large files across many machines. By large files, we are talking terabytes. YARN manages the resources and scheduling in your Hadoop framework. The MapReduce engine allows you to process data in parallel.

There are several other projects that can be installed to work with the Hadoop framework. In this chapter, you will use Hive and Ambari. Hive allows you to read and write data using SQL. You will use Hive to run the...

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