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1. Introduction
2. History of Spark
4. Hadoop vs spark
5. Components of Apache Spark
7. Applications of Spark
9. Running a Spark Application
10. Apache Spark installation on Windows
12. What is Spark Streaming
14. Features of Spark Streaming
15. Working of Spark Streaming
17. caching/persistence
18. checkpointing in spark streaming
20. What is Spark MLlib
22. Machine Learning Algorithms
23. Spark MLlib Tools
25. Machine Learning Pipelines
27. What is Spark SQL
28. Spark SQL Features
30. Spark SQL Data Frame
31. Spark SQL Data Source
33. What is PySpark
35. PySpark with Python and Scala
36. PySpark Contents
38. Companies using PySpark
40. Spark Interview Questions
Course Introduction
Introduction to Apache Spark
Configuring Apache Spark in the IDE PyCharm
Apache Spark in Cloud - DataBricks
Installing Apache Spark in Local Mode on Windows
Distributed Processing Model of Spark
PySpark Shell
Creating Spark Cluster using Google Cloud
Introduction to DataFrame
Creating Spark Project - Build Configuration
Configuring Spark Project - Application Logs
Creating AND Configuring Spark Session
Spark API's
Reading CSV, JSON and Parquet files
Creating Spark DataFrame Schema
Writing Data using DataFrame
Working with DataFrame Rows
DataFrame Rows and Unit Testing
Working with DataFrame Columns
Creating and Using User Defined Functions
Simple Aggregations
Grouping Aggregations
Window Aggregations
Inner Join in DataFrame
Course Wrap up