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Advanced Data Analytics with PySpark

When you feel constrained by the computing power of a single computer, you can leverage the Apache Spark platform's massively parallel processing capabilities using PySpark, a Python-based language...

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$1,460 USD
Course Code WA2936
Duration 2 days
Available Formats Classroom, Virtual

When you feel constrained by the computing power of a single computer, you can leverage the Apache Spark platform's massively parallel processing capabilities using PySpark, a Python-based language supported by Spark. Along with introducing PySpark, this course covers Spark Shell to interactively explore and manipulate data. Spark SQL is introduced for a uniform programming API to work with structured data. The course ends with covering Pandas for data manipulation and analysis and data visualization with seaborn.

Skills Gained

  • Learn PySpark Shell Environment
  • Understand Spark DataFrames
  • Process Data with the PySpark DataFrame API
  • Work with Pivot Tables in PySpark
  • Perform Data Visualization and Exploratory Data Analysis (EDA) in PySpark

Who Can Benefit

  • Business Analysts who want a scalable platform for solving SQL-centric problem

Prerequisites

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

Course Details

Outline

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)
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