What We Will Learn

Get a brief introduction to what we’ll learn in this course.

We'll cover the following

What we will gain

  • Master the intricacies of the big data ecosystem, encompassing data ingestion, processing, and storage options.

  • Build a robust base in distributed systems, delving into concepts like parallel processing and partitioning for scalable computing.

  • Hone our skills in PySpark for seamless data processing, transformation, and analysis. Explore diverse data types, Spark SQL, and machine learning algorithms.

  • Learn best practices to optimize PySpark performance while handling extensive datasets efficiently.

  • Integrate PySpark effortlessly with other leading big data tools, like Hadoop, Apache Hive, and Apache Kafka, for enhanced capabilities.

  • Explore real-world use cases, applying PySpark to solve complex problems through hands-on projects, gaining practical insights into big data processing and analysis.

Course prerequisites

To get the most out of this course, we should have the following prerequisites:

  • Proficiency in Python programming covering data types, control structures, functions, and classes.

  • Familiarity with SQL syntax for data querying and manipulation and know how to read and write queries.

  • Machine learning concepts will be beneficial for understanding certain sections of the course.

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Course structure

The course is structured into eight comprehensive chapters. The initial chapter provides an introduction to the foundational concepts of big data, encompassing the components of the big data pipeline: data ingestion, processing, storage, and distributed computing. Subsequent chapters delve deeper into PySpark, exploring its functionalities in data processing, analysis, and optimization within the big data pipeline. Additionally, a dedicated chapter focuses on PySpark’s integration with other prominent tools in the big data landscape. The course also includes hands-on exercises and quizzes to reinforce practical application and learning of the concepts of PySpark.

Chapter Number

Chapter Title

Chapter Description

1

Introduction to Big Data

This chapter provides an in-depth understanding of big data processing, covering various sources of big data, the associated challenges and opportunities, different processing and storage systems, and fundamental concepts related to data ingestion.

2

Exploring PySpark Core and RDDs

This chapter focuses on PySpark, the Python API for Apache Spark, uncovering its architecture and core functionalities, as includes a detailed exploration of PySpark Resilient Distributed Datasets (RDDs), emphasizing their role in data processing.

3

PySpark DataFrames and SQL

This chapter introduces PySpark DataFrames and SQL, offering insights into leveraging these powerful abstractions for streamlined and intuitive data processing tasks within PySpark.

4

Machine Learning with PySpark

This chapter outlines the fundamental concepts of machine learning and initiates learners to the PySpark MLlib, providing essential preliminary steps before diving into the modeling aspect.

5

Modeling with PySpark MLlib

This chapter empowers us with practical knowledge on utilizing PySpark MLlib for both supervised and unsupervised tasks, guiding us through the implementation of various machine learning models

6

Performance Optimization in PySpark

This chapter covers performance optimization techniques specific to PySpark, equipping us with best practices and strategies to enhance processing efficiency for large-scale datasets.

7

Integrating PySpark with Other Big Data Tools

This chapter explores the integration capabilities of PySpark with pivotal big data tools like Hadoop, Hive, Kafka, and others, emphasizing seamless interoperability within diverse data ecosystems.

8

Wrapup

This chapter summarizes key insights, consolidating the learning journey, and presenting a comprehensive conclusion, ensuring we have a clear overview of the entire course content and its practical implications.