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Scala and Spark for Big Data Analytics

You're reading from   Scala and Spark for Big Data Analytics Explore the concepts of functional programming, data streaming, and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Length 796 pages
Edition 1st Edition
Languages
Concepts
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Authors (2):
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Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
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Toc

Table of Contents (19) Chapters Close

Preface 1. Introduction to Scala 2. Object-Oriented Scala FREE CHAPTER 3. Functional Programming Concepts 4. Collection APIs 5. Tackle Big Data – Spark Comes to the Party 6. Start Working with Spark – REPL and RDDs 7. Special RDD Operations 8. Introduce a Little Structure - Spark SQL 9. Stream Me Up, Scotty - Spark Streaming 10. Everything is Connected - GraphX 11. Learning Machine Learning - Spark MLlib and Spark ML 12. My Name is Bayes, Naive Bayes 13. Time to Put Some Order - Cluster Your Data with Spark MLlib 14. Text Analytics Using Spark ML 15. Spark Tuning 16. Time to Go to ClusterLand - Deploying Spark on a Cluster 17. Testing and Debugging Spark 18. 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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