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Hands-On Big Data Modeling

You're reading from   Hands-On Big Data Modeling Effective database design techniques for data architects and business intelligence professionals

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Product type Paperback
Published in Nov 2018
Publisher Packt
ISBN-13 9781788620901
Length 306 pages
Edition 1st Edition
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Concepts
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Authors (3):
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James Lee James Lee
Author Profile Icon James Lee
James Lee
Tao Wei Tao Wei
Author Profile Icon Tao Wei
Tao Wei
Suresh Kumar Mukhiya Suresh Kumar Mukhiya
Author Profile Icon Suresh Kumar Mukhiya
Suresh Kumar Mukhiya
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Toc

Table of Contents (17) Chapters Close

Preface 1. Introduction to Big Data and Data Management 2. Data Modeling and Management Platforms FREE CHAPTER 3. Defining Data Models 4. Categorizing Data Models 5. Structures of Data Models 6. Modeling Structured Data 7. Modeling with Unstructured Data 8. Modeling with Streaming Data 9. Streaming Sensor Data 10. Concept and Approaches of Big-Data Management 11. DBMS to BDMS 12. Modeling Bitcoin Data Points with Python 13. Modeling Twitter Feeds Using Python 14. Modeling Weather Data Points with Python 15. Modeling IMDb Data Points with Python 16. Other Books You May Enjoy

Exploring streaming sensor data from a weather station

Let's get our hands dirty with steaming data. Assuming that you have your virtual machine on and ready to use, let's start with these steps:

  1. Open a Terminal shell. Change into the sensor directory. In this case, suppose that you have downloaded them into the Downloads folder, as follows:
cd Downloads/HandsOnBigData/CH09/sensor
  1. View the streaming weather station data. Run stream-data.py to see the streaming data from the weather station:
./stream-data.py

Running the preceding script will generate output similar to the following:

The measurements are appearing as they are produced by the weather station. By looking at the timestamp, we can see that the data arrives about every second. Additionally, different measurement types are produced at different frequencies. For example, R1 is measured every second, but...

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