Advanced Data Analytics with PySpark Virtual Classroom Live December 16, 2024

Course Overview

Chapter 1. Introduction to Apache Spark

• What is Apache Spark
• The Spark Platform
• Spark vs Hadoop's MapReduce (MR)
• Common Spark Use Cases
• Languages Supported by Spark
• Running Spark on a Cluster
• The Spark Application Architecture
• The Driver Process
• The Executor and Worker Processes
• Spark Shell
• Jupyter Notebook Shell Environment
• Spark Applications
• The spark-submit Tool
• The spark-submit Tool Configuration
• Interfaces with Data Storage Systems
• Project Tungsten
• The Resilient Distributed Dataset (RDD)
• Datasets and DataFrames
• Spark SQL, DataFrames, and Catalyst Optimizer
• Spark Machine Learning Library
• GraphX
• Extending Spark Environment with Custom Modules and Files
• Summary

Chapter 2. The Spark Shell

• The Spark Shell
• The Spark v.2 + Command-Line Shells
• The Spark Shell UI
• Spark Shell Options
• Getting Help
• Jupyter Notebook Shell Environment
• Example of a Jupyter Notebook Web UI (Databricks Cloud)
• The Spark Context (sc) and Spark Session (spark)
• Creating a Spark Session Object in Spark Applications
• The Shell Spark Context Object (sc)
• The Shell Spark Session Object (spark)
• Loading Files
• Saving Files
• Summary

Chapter 3. Introduction to Spark SQL

• What is Spark SQL?
• Uniform Data Access with Spark SQL
• Hive Integration
• Hive Interface
• Integration with BI Tools
• What is a DataFrame?
• Creating a DataFrame in PySpark
• Commonly Used DataFrame Methods and Properties in PySpark
• Grouping and Aggregation in PySpark
• The "DataFrame to RDD" Bridge in PySpark
• The SQLContext Object
• Examples of Spark SQL / DataFrame (PySpark Example)
• Converting an RDD to a DataFrame Example
• Example of Reading / Writing a JSON File
• Using JDBC Sources
• JDBC Connection Example
• Performance, Scalability, and Fault-tolerance of Spark SQL
• Summary

Chapter 4. Practical Introduction to Pandas

• What is pandas?
• The Series Object
• Accessing Values and Indexes in Series
• Setting Up Your Own Index
• Using the Series Index as a Lookup Key
• Can I Pack a Python Dictionary into a Series?
• The DataFrame Object
• The DataFrame's Value Proposition
• Creating a pandas DataFrame
• Getting DataFrame Metrics
• Accessing DataFrame Columns
• Accessing DataFrame Rows
• Accessing DataFrame Cells
• Using iloc
• Using loc
• Examples of Using loc
• DataFrames are Mutable via Object Reference!
• Deleting Rows and Columns
• Adding a New Column to a DataFrame
• Appending / Concatenating DataFrame and Series Objects
• Example of Appending / Concatenating DataFrames
• Re-indexing Series and DataFrames
• Getting Descriptive Statistics of DataFrame Columns
• Getting Descriptive Statistics of DataFrames
• Applying a Function
• Sorting DataFrames
• Reading From CSV Files
• Writing to the System Clipboard
• Writing to a CSV File
• Fine-Tuning the Column Data Types
• Changing the Type of a Column
• What May Go Wrong with Type Conversion
• Summary

Chapter 5. Data Visualization with seaborn in Python

• Data Visualization
• Data Visualization in Python
• Matplotlib
• Getting Started with matplotlib
• Figures
• Saving Figures to a File
• Seaborn
• Getting Started with seaborn
• Histograms and KDE
• Plotting Bivariate Distributions
• Scatter plots in seaborn
• Pair plots in seaborn
• Heatmaps
• Summary

Chapter 6. (Optional) Quick Introduction to Python for Data Engineers

• What is Python?
• Additional Documentation
• Which version of Python am I running?
• Python Dev Tools and REPLs
• IPython
• Jupyter
• Jupyter Operation Modes
• Jupyter Common Commands
• Anaconda
• Python Variables and Basic Syntax
• Variable Scopes
• PEP8
• The Python Programs
• Getting Help
• Variable Types
• Assigning Multiple Values to Multiple Variables
• Null (None)
• Strings
• Finding Index of a Substring
• String Splitting
• Triple-Delimited String Literals
• Raw String Literals
• String Formatting and Interpolation
• Boolean
• Boolean Operators
• Numbers
• Looking Up the Runtime Type of a Variable
• Divisions
• Assignment-with-Operation
• Comments:
• Relational Operators
• The if-elif-else Triad
• An if-elif-else Example
• Conditional Expressions (a.k.a. Ternary Operator)
• The While-Break-Continue Triad
• The for Loop
• try-except-finally
• Lists
• Main List Methods
• Dictionaries
• Working with Dictionaries
• Sets
• Common Set Operations
• Set Operations Examples
• Finding Unique Elements in a List
• Enumerate
• Tuples
• Unpacking Tuples
• Functions
• Dealing with Arbitrary Number of Parameters
• Keyword Function Parameters
• The range Object
• Random Numbers
• Python Modules
• Importing Modules
• Installing Modules
• Listing Methods in a Module
• Creating Your Own Modules
• Creating a Runnable Application
• List Comprehension
• Zipping Lists
• Working with Files
• Reading and Writing Files
• Reading Command-Line Parameters
• Accessing Environment Variables
• What is Functional Programming (FP)?
• Terminology: Higher-Order Functions
• Lambda Functions in Python
• Example: Lambdas in the Sorted Function
• Other Examples of Using Lambdas
• Regular Expressions
• Using Regular Expressions Examples
• Python Data Science-Centric Libraries
• Summary

Lab Exercises

• Lab 1. Learning the Databricks Community Cloud Lab Environment
• Lab 2. Learning PySpark Shell Environment
• Lab 3. Understanding Spark DataFrames
• Lab 4. Learning the PySpark DataFrame API
• Lab 5. Processing Data in PySpark using the DataFrame API (Project)
• Lab 6. Working with Pivot Tables in PySpark (Project)
• Lab 7. Data Visualization and EDA in PySpark
• Lab 8. Data Visualization and EDA in PySpark (Project)

Prerequisites

Knowledge of SQL, familiarity with Python (or the ability to learn the basics of a new language).