Big Data Fundamentals with PySpark
Upendra Devisetty
Science Analyst, CyVerse
Chapter 1: Fundamentals of BigData and introduction to Spark as a distributed computing framework
Main components: Spark Core and Spark built-in libraries - Spark SQL, Spark MLlib, Graphx, and Spark Streaming
PySpark: Apache Spark’s Python API to execute Spark jobs
PySpark shell: For developing the interactive applications in python
Spark modes: Local and cluster mode
Chapter 2: Introduction to RDDs, different features of RDDs, methods of creating RDDs and RDD operations (Transformations and Actions)
Chapter 3: Introduction to Spark SQL, DataFrame abstraction, creating DataFrames, DataFrame operations and visualizing Big Data through DataFrames
Chapter 4: Introduction to Spark MLlib, the three C's of Machine Learning (Collaborative filtering, Classification and Clustering)
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