Data Engineering Bootcamp Training using Python and PySpark Virtual Classroom Live December 09, 2024

Course Overview

Big Data Concepts and Systems Overview for Data Engineers

• Gartner's Definition of Big Data
• The Big Data Confluence Diagram
• A Practical Definition of Big Data
• Challenges Posed by Big Data
• The Traditional Client - Server Processing Pattern
• Enter Distributed Computing
• Data Physics
• Data Locality (Distributed Computing Economics)
• The CAP Theorem
• Mechanisms to Guarantee a Single CAP Property
• Eventual Consistency
• NoSQL Systems CAP Triangle
• Big Data Sharding
• Sharding Example
• Apache Hadoop
• Hadoop Ecosystem Projects
• Other Hadoop Ecosystem Projects
• Hadoop Design Principles
• Hadoop's Main Components
• Hadoop Simple Definition
• Hadoop Component Diagram
• HDFS
• Storing Raw Data in HDFS and Schema-on-Demand
• MapReduce Defined
• MapReduce Shared-Nothing Architecture
• MapReduce Phases
• The Map Phase
• The Reduce Phase
• Similarity with SQL Aggregation Operations

Defining Data Engineering

• Data is King
• Translating Data into Operational and Business Insights
• What is Data Engineering
• The Data-Related Roles
• The Data Science Skill Sets
• The Data Engineer Role
• Core Skills and Competencies
• An Example of a Data Product
• What is Data Wrangling (Munging)?
• The Data Exchange Interoperability Options

Data Processing Phases

• Typical Data Processing Pipeline
• Data Discovery Phase
• Data Harvesting Phase
• Data Priming Phase
• Exploratory Data Analysis
• Model Planning Phase
• Model Building Phase
• Communicating the Results
• Production Roll-out
• Data Logistics and Data Governance
• Data Processing Workflow Engines
• Apache Airflow
• Data Lineage and Provenance
• Apache NiFi

Python 3 Introduction

• What is Python?
• Python Documentation
• Where Can I Use Python?
• Which version of Python am I running?
• Running Python Programs
• Python Shell
• Dev Tools and REPLs
• IPython
• Jupyter
• The Anaconda Python Distribution

Python Variables and Types

• Variables and Types
• More on Variables
• Assigning Multiple Values to Multiple Variables
• More on Types
• Variable Scopes
• The Layout of Python Programs
• Comments and Triple-Delimited String Literals
• Sample Python Code
• PEP8
• Getting Help on Python Objects
• Null (None)
• Strings
• Finding Index of a Substring
• String Splitting
• Raw String Literals
• String Formatting and Interpolation
• String Public Method Names
• The Boolean Type
• Boolean Operators
• Relational Operators
• Numbers
• \"Easy Numbers\"
• Looking Up the Runtime Type of a Variable
• Divisions
• Assignment-with-Operation
• Dates and Times

Control Statements and Data Collections

• Control Flow with 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
• The range() Function
• Examples of Using range()
• The try-except-finally Construct
• The assert Expression
• Lists
• Main List Methods
• List Comprehension
• Zipping Lists
• Enumerate
• Dictionaries
• Working with Dictionaries
• Other Dictionary Methods
• Sets
• Set Methods
• Set Operations
• Set Operations Examples
• Finding Unique Elements in a List
• Common Collection Functions and Operators
• Tuples
• Unpacking Tuples

Functions and Modules

• Built-in Functions
• Functions
• The \"Call by Sharing\" Parameter Passing
• Global and Local Variable Scopes
• Default Parameters
• Named Parameters
• Dealing with Arbitrary Number of Parameters
• Keyword Function Parameters
• What is Functional Programming (FP)?
• Concept: Pure Functions
• Concept: Recursion
• Concept: Higher-Order Functions
• Lambda Functions in Python
• Examples of Using Lambdas
• Lambdas in the Sorted Function
• Python Modules
• Importing Modules
• Installing Modules
• Listing Methods in a Module
• Creating Your Own Modules
• Creating a Module's Entry Point

File I/O and Useful Modules

• Reading Command-Line Parameters
• Hands-On Exercise (N/A in DCC)
• Working with Files
• Reading and Writing Files
• Random Numbers
• Regular Expressions
• The re Object Methods
• Using Regular Expressions Examples

Practical Introduction to NumPy

• NumPy
• The First Take on NumPy Arrays
• The ndarray Data Structure
• Getting Help
• Understanding Axes
• Indexing Elements in a NumPy Array
• Understanding Types
• Re-Shaping
• Commonly Used Array Metrics
• Commonly Used Aggregate Functions
• Sorting Arrays
• Vectorization
• Vectorization Visually
• Broadcasting
• Broadcasting Visually
• Filtering
• Array Arithmetic Operations
• Reductions: Finding the Sum of Elements by Axis
• Array Slicing
• 2-D Array Slicing
• Slicing and Stepping Through
• The Linear Algebra Functions

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!
• The Axes
• 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
• Navigating Rows and Columns For Data Reduction
• 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

Data Grouping and Aggregation with pandas

• Data Aggregation and Grouping
• Sample Data Set
• The pandas.core.groupby.SeriesGroupBy Object
• Grouping by Two or More Columns
• Emulating SQL's WHERE Clause
• The Pivot Tables
• Cross-Tabulation

Repairing and Normalizing Data

• Repairing and Normalizing Data
• Dealing with the Missing Data
• Sample Data Set
• Getting Info on Null Data
• Dropping a Column
• Interpolating Missing Data in pandas
• Replacing the Missing Values with the Mean Value
• Scaling (Normalizing) the Data
• Data Preprocessing with scikit-learn
• Scaling with the scale() Function
• The MinMaxScaler Object

Data Visualization in Python

• Data Visualization
• Data Visualization in Python
• Matplotlib
• Getting Started with matplotlib
• The matplotlib.pyplot.plot() Function
• The matplotlib.pyplot.bar() Function
• The matplotlib.pyplot.pie () Function
• The matplotlib.pyplot.subplot() Function
• A Subplot Example
• 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
• A Seaborn Scatterplot with Varying Point Sizes and Hues
• ggplot

Python as a Cloud Scripting Language

• Python's Value
• Python on AWS
• AWS SDK For Python (boto3)
• What is Serverless Computing?
• How Functions Work
• The AWS Lambda Event Handler
• What is AWS Glue?
• PySpark on Glue - Sample Script

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
• The Resilient Distributed Dataset (RDD)
• Datasets and DataFrames
• Spark SQL, DataFrames, and Catalyst Optimizer
• Project Tungsten
• Spark Machine Learning Library
• Spark (Structured) Streaming
• GraphX
• Extending Spark Environment with Custom Modules and Files
• Spark 3
• Spark 3 Updates at a Glance

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

Spark RDDs

• The Resilient Distributed Dataset (RDD)
• Ways to Create an RDD
• Supported Data Types
• RDD Operations
• RDDs are Immutable
• Spark Actions
• RDD Transformations
• Other RDD Operations
• Chaining RDD Operations
• RDD Lineage
• The Big Picture
• What May Go Wrong
• Miscellaneous Pair RDD Operations
• RDD Caching

Parallel Data Processing with Spark

• Running Spark on a Cluster
• Data Partitioning
• Data Partitioning Diagram
• Single Local File System RDD Partitioning
• Multiple File RDD Partitioning
• Special Cases for Small-sized Files
• Parallel Data Processing of Partitions
• Spark Application, Jobs, and Tasks
• Stages and Shuffles
• The "Big Picture"

Introduction to Spark SQL

• What is Spark SQL?
• Uniform Data Access with Spark SQL
• Using JDBC Sources
• Hive Integration
• 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
• Performance, Scalability, and Fault-tolerance of Spark SQL

Lab Exercises

• Lab 1. Data Availability and Consistency
• Lab 2. A/B Testing Data Engineering Tasks Project
• Lab 3. Learning the Databricks Community Cloud Lab Environment
• Lab 4. Python Variables
• Lab 5. Dates and Times
• Lab 6. The if, for, and try Constructs
• Lab 7. Understanding Lists
• Lab 8. Dictionaries
• Lab 9. Sets
• Lab 10. Tuples
• Lab 11. Functions
• Lab 12. Functional Programming
• Lab 13. File I/O
• Lab 14. Using HTTP and JSON
• Lab 15. Random Numbers
• Lab 16. Regular Expressions
• Lab 17. Understanding NumPy
• Lab 18. A NumPy Project
• Lab 19. Understanding pandas
• Lab 20. Data Grouping and Aggregation
• Lab 21. Repairing and Normalizing Data
• Lab 22. Data Visualization and EDA with pandas and seaborn
• Lab 23. Correlating Cause and Effect
• Lab 24. Learning PySpark Shell Environment
• Lab 25. Understanding Spark DataFrames
• Lab 26. Learning the PySpark DataFrame API
• Lab 27. Data Repair and Normalization in PySpark
• Lab 28. Working with Parquet File Format in PySpark and pandas

Prerequisites

• Some working experience in any programming language (the students will be introduced to programming in Python).
• Basic understanding of SQL and data processing concepts, including data grouping and aggregation.