Scala and Spark for Big Data Analytics Explore the concepts of functional programming data streaming and machine learning 1st Edition by Rezaul Karim, Sridlhar Alla ISBN 1785280848 978-1785280849

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ISBN 10: 1785280848
ISBN 13: 978-1785280849
Author: Rezaul Karim, Sridlhar Alla

Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you.

The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment.

You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio.

By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.

Scala and Spark for Big Data Analytics Explore the concepts of functional programming data streaming and machine learning 1st Table of contents:

1. Nested functions
2. Import statements
3. Operators as methods
4. Methods and parameter lists
5. Methods inside methods
6. Constructor in Scala
7. Objects instead of static methods
8. Traits
9. Scala for the beginners
10. Your first line of code
11. I’m the hello world program, explain me well!
12. Run Scala interactively!
13. Compile it!
14. Execute it with Scala command
15. Summary
16. Object-Oriented Scala
17. Variables in Scala
18. Reference versus value immutability
19. Data types in Scala
20. Variable initialization
21. Type annotations
22. Type ascription
23. Lazy val
24. Methods, classes, and objects in Scala
25. Methods in Scala
26. The return in Scala
27. Classes in Scala
28. Objects in Scala
29. Singleton and companion objects
30. Companion objects
31. Comparing and contrasting: val and final
32. Access and visibility
33. Constructors
34. Traits in Scala
35. A trait syntax
36. Extending traits
37. Abstract classes
38. Abstract classes and the override keyword
39. Case classes in Scala
40. Packages and package objects
41. Java interoperability
42. Pattern matching
43. Implicit in Scala
44. Generic in Scala
45. Defining a generic class
46. SBT and other build systems
47. Build with SBT
48. Maven with Eclipse
49. Gradle with Eclipse
50. Summary
51. Functional Programming Concepts
52. Introduction to functional programming
53. Advantages of functional programming
54. Functional Scala for the data scientists
55. Why FP and Scala for learning Spark?
56. Why Spark?
57. Scala and the Spark programming model
58. Scala and the Spark ecosystem
59. Pure functions and higher-order functions
60. Pure functions
61. Anonymous functions
62. Higher-order functions
63. Function as a return value
64. Using higher-order functions
65. Error handling in functional Scala
66. Failure and exceptions in Scala
67. Throwing exceptions
68. Catching exception using try and catch
69. Finally
70. Creating an Either
71. Future
72. Run one task, but block
73. Functional programming and data mutability
74. Summary
75. Collection APIs
76. Scala collection APIs
77. Types and hierarchies
78. Traversable
79. Iterable
80. Seq, LinearSeq, and IndexedSeq
81. Mutable and immutable
82. Arrays
83. Lists
84. Sets
85. Tuples
86. Maps
87. Option
88. Exists
89. Forall
90. Filter
91. Map
92. Take
93. GroupBy
94. Init
95. Drop
96. TakeWhile
97. DropWhile
98. FlatMap
99. Performance characteristics
100. Performance characteristics of collection objects
101. Memory usage by collection objects
102. Java interoperability
103. Using Scala implicits
104. Implicit conversions in Scala
105. Summary
106. Tackle Big Data – Spark Comes to the Party
107. Introduction to data analytics
108. Inside the data analytics process
109. Introduction to big data
110. 4 Vs of big data
111. Variety of Data
112. Velocity of Data
113. Volume of Data
114. Veracity of Data
115. Distributed computing using Apache Hadoop
116. Hadoop Distributed File System (HDFS)
117. HDFS High Availability
118. HDFS Federation
119. HDFS Snapshot
120. HDFS Read
121. HDFS Write
122. MapReduce framework
123. Here comes Apache Spark
124. Spark core
125. Spark SQL
126. Spark streaming
127. Spark GraphX
128. Spark ML
129. PySpark
130. SparkR
131. Summary
132. Start Working with Spark – REPL and RDDs
133. Dig deeper into Apache Spark
134. Apache Spark installation
135. Spark standalone
136. Spark on YARN
137. YARN client mode
138. YARN cluster mode
139. Spark on Mesos
140. Introduction to RDDs
141. RDD Creation
142. Parallelizing a collection
143. Reading data from an external source
144. Transformation of an existing RDD
145. Streaming API
146. Using the Spark shell
147. Actions and Transformations
148. Transformations
149. General transformations
150. Math/Statistical transformations
151. Set theory/relational transformations
152. Data structure-based transformations
153. map function
154. flatMap function
155. filter function
156. coalesce
157. repartition
158. Actions
159. reduce
160. count
161. collect
162. Caching
163. Loading and saving data
164. Loading data
165. textFile
166. wholeTextFiles
167. Load from a JDBC Datasource
168. Saving RDD
169. Summary
170. Special RDD Operations
171. Types of RDDs
172. Pair RDD
173. DoubleRDD
174. SequenceFileRDD
175. CoGroupedRDD
176. ShuffledRDD
177. UnionRDD
178. HadoopRDD
179. NewHadoopRDD
180. Aggregations
181. groupByKey
182. reduceByKey
183. aggregateByKey
184. combineByKey
185. Comparison of groupByKey, reduceByKey, combineByKey, and aggregateByKey
186. Partitioning and shuffling
187. Partitioners
188. HashPartitioner
189. RangePartitioner
190. Shuffling
191. Narrow Dependencies
192. Wide Dependencies
193. Broadcast variables
194. Creating broadcast variables
195. Cleaning broadcast variables
196. Destroying broadcast variables
197. Accumulators
198. Summary
199. Introduce a Little Structure – Spark SQL
200. Spark SQL and DataFrames
201. DataFrame API and SQL API
202. Pivots
203. Filters

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Tags: Rezaul Karim, Sridlhar Alla, Big Data, functional programming