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PySpark Recipes: A Problem-Solution Approach with PySpark2
Chapter 1: The era of Big Data and HadoopChapter learns about Big data and its usefulness. Also how Hadoop and its ecosystem is beautifully able to process big data for useful informations. What are the shortcomings of Hadoop which requires another Big data processing platform.No of pages 15-20Sub -Topics1. Introduction to Big-Data2. Big Data challenges and processing technology 3. Hadoop, structure and its ecosystem4. Shortcomings of Hadoop
Chapter 2: Python, NumPy and SciPyChapter goal of this chapter to get reader acquainted with Python, NumPy and SciPy.
No of 25-30Sub - Topics 1. Introduction to Python2. Python collection, String Function and Class3. NumPy and ndarray4. SciPyChapter 3: Spark : Introduction, Installation, Structure and PySparkChapter chapters will introduce Spark, Installation on Single machine. There after it continues with structure of Spark. Finally, PySpark is introduced.No of pages : 15-20Sub - 1. Introduction to Spark2. Spark installation on Ubuntu3. Spark architecture4. PySpark and Its architecture
Chapter 4: Resilient Distributed Dataset (RDD)Chapter deals with the core of Spark, RDD. Operation on RDDNo of 25-30Sub - 1. Introduction to RDD and its characteristics2. Transformation and Actions2. Operations on RDD ( like map, filter, set operations and many more)
Chapter 5: The power of pairs : Paired RDDChapter RDD can help in making many complex computation easy in programming. Learners will learn paired RDD and operation on this.No of -20Sub - 1. Introduction to Paired RDD2. Operation on paired RDD (mapByKey, reduceByKey ......) Chapter 6: Advance PySpark and PySpark application optimizationChapter 30-35Reader will learn about Advance PySpark topics broadcast and accumulator. In this chapter learner will learn about PySpark application optimization. No of - 1. Spark Accumulator2. Spark Broadcast3. Spark Code Optimization
Chapter 7: IO in PySparkChapter will learn PySpark IO in this chapter. Reading and writing .csv file and .json files. We will also learn how to connect to different databases with PySpark.No of - 1. Reading and writing JSON and .csv files2. Reading data from HDFS3. Reading data from different databases and writing data to different databases
Chapter 8: PySpark StreamingChapter will understand real time data analysis with PySpark Streaming. This chapter is focus on PySpark Streaming architecture, Discretized stream operations and windowing operations.No of - 1. PySpark Streaming architecture2. Discretized Stream and operations3. Concept of windowing operations
Chapter 9: SparkSQLChapter this chapter reader will learn about SparkSQL. SparkSQL Dataframe is introduced in this chapter. In this chapter learner will learn how to use SQL commands using SparkSQLNo of 40-50Sub -
Chapter 2: Python, NumPy and SciPyChapter goal of this chapter to get reader acquainted with Python, NumPy and SciPy.
No of 25-30Sub - Topics 1. Introduction to Python2. Python collection, String Function and Class3. NumPy and ndarray4. SciPyChapter 3: Spark : Introduction, Installation, Structure and PySparkChapter chapters will introduce Spark, Installation on Single machine. There after it continues with structure of Spark. Finally, PySpark is introduced.No of pages : 15-20Sub - 1. Introduction to Spark2. Spark installation on Ubuntu3. Spark architecture4. PySpark and Its architecture
Chapter 4: Resilient Distributed Dataset (RDD)Chapter deals with the core of Spark, RDD. Operation on RDDNo of 25-30Sub - 1. Introduction to RDD and its characteristics2. Transformation and Actions2. Operations on RDD ( like map, filter, set operations and many more)
Chapter 5: The power of pairs : Paired RDDChapter RDD can help in making many complex computation easy in programming. Learners will learn paired RDD and operation on this.No of -20Sub - 1. Introduction to Paired RDD2. Operation on paired RDD (mapByKey, reduceByKey ......) Chapter 6: Advance PySpark and PySpark application optimizationChapter 30-35Reader will learn about Advance PySpark topics broadcast and accumulator. In this chapter learner will learn about PySpark application optimization. No of - 1. Spark Accumulator2. Spark Broadcast3. Spark Code Optimization
Chapter 7: IO in PySparkChapter will learn PySpark IO in this chapter. Reading and writing .csv file and .json files. We will also learn how to connect to different databases with PySpark.No of - 1. Reading and writing JSON and .csv files2. Reading data from HDFS3. Reading data from different databases and writing data to different databases
Chapter 8: PySpark StreamingChapter will understand real time data analysis with PySpark Streaming. This chapter is focus on PySpark Streaming architecture, Discretized stream operations and windowing operations.No of - 1. PySpark Streaming architecture2. Discretized Stream and operations3. Concept of windowing operations
Chapter 9: SparkSQLChapter this chapter reader will learn about SparkSQL. SparkSQL Dataframe is introduced in this chapter. In this chapter learner will learn how to use SQL commands using SparkSQLNo of 40-50Sub -
292 pages, Paperback
Published December 10, 2017
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February 16, 2018
The book couldn't have been launched at a better time as it is a comprehensive book that covers the current needs of understanding the big data in a simple and very basic way. The spectrum of the content from basic installation to contextual mathemathics of topics covers almost all aspects of big data required to understand the field.
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