PySpark - Module 0

PySpark Fundamentals and Advanced Topics

Distributed computing is at the heart of all the recent advancements of Data science and AI models. We can now scale out as an alternative to scale up. And that makes it possible to retrieve data, prepare the data and train our models not only at a faster speed, but also cheaper.

One of the pioneer commercial players on the filed of distributed computing is DataBricks. This the company behind the open source Spark community.

Course Objective

In today’s data-driven world, the volume of data generated is growing exponentially. Traditional data processing systems struggle to handle this deluge efficiently. Distributed computing frameworks like Apache Spark have emerged as powerful tools for processing large datasets quickly and efficiently. Understanding Spark and PySpark (the Python API for Spark) is crucial for data professionals who want to leverage the full potential of big data. This course is designed to equip you with the skills to process and analyse large datasets using Apache Spark and PySpark, emphasizing its importance in modern data science and machine learning workflows. By the end of this course, you will have a good understanding of how to process, analyze, and manipulate large datasets efficiently. You will also gain hands-on experience in building machine learning models and optimizing Spark queries for performance.

Over 30 hours of content is prepared that would help you develop a solid understanding of distributed computing, Spark’s architecture, and how to apply Spark for big data processing. The course includes a mix of theoretical concepts and hands-on exercises, leveraging free online resources.

Course Highlights

• Fundamentals of PySpark: Learn the basics of PySpark, including its architecture, Resilient Distributed Datasets (RDDs), and DataFrames.
• Data Processing and SQL: Master the use of DataFrames and Spark SQL for data manipulation and querying.
• Streaming and Real-Time Data: Understand how to process real-time data using Spark Streaming.
• Machine Learning: Explore machine learning techniques and build models using Spark’s MLlib (for year 2025 - 2026).
• Optimization Techniques: Learn how to optimize Spark applications for better performance and efficiency (for year 2025 - 2026).
• Capstone Project: Apply your knowledge to a real-world project, demonstrating your ability to handle big data problems from start to finish (for year 2025 - 2026).

Learning Outcomes

By the end of this course, you will be able to:

• Understand and explain the architecture and components of Apache Spark.
• Install and configure PySpark on a Linux environment (WSL).
• Work with RDDs and DataFrames for data processing and analysis.
• Use Spark SQL to run queries and manipulate structured data.
• Process streaming data with Spark Streaming.
• Build and evaluate machine learning models using MLlib.
• Optimize Spark applications for improved performance.
• Apply your skills to a small capstone project.

Key Questions Answered

Throughout the course, you will be able to answer questions such as:

• What are the key components of Apache Spark, and how do they interact?
• How can you create and manipulate RDDs and DataFrames in PySpark?
• How do you use Spark SQL to perform data queries and transformations?
• What techniques are available for processing real-time data streams in Spark?
• How can you build and deploy machine learning models using Spark’s MLlib?
• What strategies can be employed to optimize Spark applications for performance?

Time Commitment

This course is designed to be completed over approximately 30 hours of self-paced study. Here is a breakdown of the estimated time required for each module:

• Module 1: Introduction to Big Data and Apache Spark: 3 hours
• Module 2: Spark Architecture and RDDs: 5 hours
• Module 3: DataFrames and Spark SQL: 6 hours
• Module 4: Data Sources and Sinks: 4 hours
• Module 5: Spark Streaming: 12 hours
• Module 6: Machine Learning with PySpark: 0 hours
• Module 7: Advanced Spark Techniques: 0 hours
• Module 8: Real-World Applications and Capstone Project: 0 hours

This structured approach ensures that you will have ample time to grasp each concept thoroughly, practice through hands-on exercises, and apply what you have learned to real-world scenarios.

Getting Started

To begin, make sure you can install and configure PySpark. Familiarize yourself with the course materials, including the recommended readings and online resources. Each module might contain practical assignments and quizzes to reinforce your learning, so take your time to complete these exercises when available.

Course Outline: PySpark Fundamentals and Advanced Topics

Module 1: Introduction to Big Data and Apache Spark

1. Introduction to Big Data
   • What is Big Data?
   • Challenges of Big Data
2. Overview of Apache Spark
   • History and evolution
   • Components of the Spark ecosystem
3. Installing PySpark
   • System requirements
   • Installing PySpark on a Linux environment (WSL)
   • Configuring PySpark and Jupyter Notebook

Module 2: Spark Architecture and RDDs

1. Spark Architecture
   • Spark driver and executors
   • SparkContext and SparkSession
   • Cluster managers (Standalone, YARN, Mesos, Kubernetes)
2. Resilient Distributed Datasets (RDDs)
   • What are RDDs?
   • Creating RDDs
   • Transformations and Actions
   • RDD Lineage and Persistence

Module 3: DataFrames and Spark SQL

1. Introduction to DataFrames

   • Difference between RDDs and DataFrames
   • Creating DataFrames
   • Schema inference and manual schema definition

2. DataFrame Operations

   • Selecting, filtering, and transforming data.
   • Aggregations and Grouping
   • Joins and unions
   • Working with semi-structured data

3. Spark SQL

   • SQL queries with Spark
   • Registering DataFrames as tables
   • Using SQL queries to manipulate DataFrames

Module 4: Data Sources and Sinks

1. Reading Data
   • Reading from CSV, JSON, Parquet, and other formats
   • Reading from databases (JDBC)
2. Writing Data
   • Writing to CSV, JSON, Parquet, and other formats
   • Writing to databases (JDBC)
3. Data Sources API
   • Introduction to the Data Sources API
   • Custom data sources

Module 5: Spark Streaming

1. Introduction to Spark Streaming
   • What is Spark Streaming?
   • DStreams and micro-batching
2. Streaming Sources
   • Reading from Kafka, Socket, and other sources
3. Streaming Operations
   • Transformations on DStreams
   • Windowed operations and stateful computations
4. Fault Tolerance and Checkpointing
   • Handling fault tolerance in streaming applications
   • Checkpointing

Module 6: Machine Learning with PySpark (0 hours)

0. hyperparameter optimization framework designed for both single-machine and distributed setups: Optuna and Hyperopt
1. Introduction to MLlib
   • Overview of MLlib
   • Data preprocessing with MLlib
2. Building Machine Learning Models
   • Supervised learning algorithms (e.g., Linear Regression, Logistic Regression, Decision Trees)
   • Unsupervised learning algorithms (e.g., K-means clustering)
3. Model Evaluation and Hyperparameter Tuning
   • Evaluating model performance
   • Cross-validation and hyperparameter tuning
4. Pipeline API
   • Building ML pipelines
   • Using feature transformers
5. Case study: Predictive modeling with MLlib

Module 7: Advanced Spark Techniques (0 hours)

1. Optimizing Spark Applications
   • Understanding Spark execution plan
   • Catalyst optimizer
   • Tungsten execution engine
2. Performance Tuning
   • Caching and persistence strategies
   • Memory management and garbage collection
   • Shuffle operations and optimization
3. Debugging and Monitoring
   • Using Spark UI
   • Logging and metrics
   • Handling and avoiding common pitfalls

Module 8: Real-World Applications and Capstone Project (0 hours)

1. Case Studies and Real-World Applications
   • Example projects using PySpark
   • Industry use cases
2. Capstone Project
   • Define a problem statement
   • Design and implement a PySpark solution
   • Optimize and present findings

Module 9: Advanced Features

• Working with GraphX for graph processing

Learning Resources

• Documentation and Tutorials
  • Apache Spark official documentation
  • PySpark API reference
• Books and Online Courses
  • “Learning PySpark” by Tomasz Drabas and Denny Lee
  • Online courses on platforms like Coursera, Udacity, and edX
• Community and Support
  • GitHub repositories for sample projects

Assessment

• Quizzes
  • You will be provided with quizzes to reinforce learning
• Capstone Project
  • Depending on your track (Data Science or Data Engineering) you will do a small project that requires max a day to complete and deliver.

You are offered the opportunity to earn extra credit through the following two deliverables, which are designed to reinforce and showcase your understanding of the material. Please pay close attention to the instructions and deadlines.

By the end of this material you will be able to provide answers to the questions bellow:

1. Describe the PySpark architecture.
2. What are RDDs in PySpark?
3. Explain the concept of lazy evaluation in PySpark.
4. How does PySpark differ from Apache Hadoop?
5. What are DataFrames in PySpark?
6. How do you initialize a SparkSession?
7. What is the significance of the SparkContext?
8. Describe the types of transformations in PySpark.
9. How do you read a CSV file into a PySpark DataFrame?
10. What are actions in PySpark, and how do they differ from transformations?
11. How can you filter rows in a DataFrame?
12. Explain how to perform joins in PySpark.
13. How do you aggregate data in PySpark?
14. What are UDFs (User Defined Functions), and how are they used?
15. How can you handle missing or null values in PySpark?
16. How do you repartition a DataFrame, and why?
17. Describe how to cache a DataFrame. Why is it useful?
18. How do you save a DataFrame to a file?
19. Explain the concept of partitioning in PySpark.
20. How can broadcast variables improve performance?
21. What are accumulators, and how are they used?
22. How does PySpark handle data skewness?
23. Explain how checkpointing works in PySpark.
24. What is delta lake? Look at here and here!
25. What is data lakehouse architecture.