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Scala and Spark for Big Data Analytics

You're reading from  Scala and Spark for Big Data Analytics

Product type Book
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Pages 796 pages
Edition 1st Edition
Languages
Concepts
Authors (2):
Md. Rezaul Karim Md. Rezaul Karim
Profile icon Md. Rezaul Karim
Sridhar Alla Sridhar Alla
Profile icon Sridhar Alla
View More author details

Table of Contents (19) Chapters

Preface Introduction to Scala Object-Oriented Scala Functional Programming Concepts Collection APIs Tackle Big Data – Spark Comes to the Party Start Working with Spark – REPL and RDDs Special RDD Operations Introduce a Little Structure - Spark SQL Stream Me Up, Scotty - Spark Streaming Everything is Connected - GraphX Learning Machine Learning - Spark MLlib and Spark ML My Name is Bayes, Naive Bayes Time to Put Some Order - Cluster Your Data with Spark MLlib Text Analytics Using Spark ML Spark Tuning Time to Go to ClusterLand - Deploying Spark on a Cluster Testing and Debugging Spark PySpark and SparkR

Aggregations

Aggregation is the method of collecting data based on a condition and performing analytics on the data. Aggregation is very important to make sense of data of all sizes, as just having raw records of data is not that useful for most use cases.

For example, if you look at the following table and then the aggregated view, it is obvious that just raw records do not help you understand the data.

Imagine a table containing one temperature measurement per day for every city in the world for five years.

Shown in the following is a table containing records of average temperature per day per city:

City

Date Temperature
Boston 12/23/2016 32
New York 12/24/2016 36
Boston 12/24/2016 30
Philadelphia 12/25/2016 34
Boston 12/25/2016 28

If we want to compute the average temperature per city for all the days we have measurements for in the above table, we can see...

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