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Practical Real-time Data Processing and Analytics

You're reading from   Practical Real-time Data Processing and Analytics Distributed Computing and Event Processing using Apache Spark, Flink, Storm, and Kafka

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787281202
Length 360 pages
Edition 1st Edition
Languages
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Authors (2):
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Shilpi Saxena Shilpi Saxena
Author Profile Icon Shilpi Saxena
Shilpi Saxena
Saurabh Gupta Saurabh Gupta
Author Profile Icon Saurabh Gupta
Saurabh Gupta
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Toc

Table of Contents (14) Chapters Close

Preface 1. Introducing Real-Time Analytics FREE CHAPTER 2. Real Time Applications – The Basic Ingredients 3. Understanding and Tailing Data Streams 4. Setting up the Infrastructure for Storm 5. Configuring Apache Spark and Flink 6. Integrating Storm with a Data Source 7. From Storm to Sink 8. Storm Trident 9. Working with Spark 10. Working with Spark Operations 11. Spark Streaming 12. Working with Apache Flink 13. Case Study

Data modeling

The real-time vehicle sensor data model is:

Field name

Vehicle Id
Latitude Longitude Speed Timestamp

Data type

String

Double

Double

Integer

Long

 

The Vehicle Id is the unique identifier of any vehicle. Generally, it is the chassis number shown in different locations for different types of vehicle. Latitude and Longitude are detected by GPS which tells us the current location. Speed is the speed of the vehicle. Timestamp is the time when this event was generated.

The static vehicle data model is:

Field name

Vehicle Id

Latitude

Longitude

Distance

Phone number

Data type

String

Double

Double

Double

String

 

This static data is provided by the owner of the vehicle while setting up the alert for his/her vehicle. The Vehicle Id is the unique identifier of any vehicle. Latitude and Longitude are the starting...

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