Mastering Big Data with PySpark

Gain insights into PySpark within big data. Learn about data ingestion, distributed computing, data processing, and performance optimization to solve real-world problems and apply machine learning.

Beginner

48 Lessons

12h

Certificate of Completion

Gain insights into PySpark within big data. Learn about data ingestion, distributed computing, data processing, and performance optimization to solve real-world problems and apply machine learning.

AI-POWERED

Explanations

AI-POWERED

Explanations

This course includes

79 Playgrounds

5 Quizzes

This course includes

79 Playgrounds

5 Quizzes

Course Overview

This course explores the big data ecosystem, focusing on hands-on utilization of PySpark—the Python API for Apache Spark. In this course, you’ll experience a balanced blend of theory and practice. You’ll learn about data ingestion, storage, distributed computing, PySpark’s intricacies, data processing, data analysis, performance optimization, tool integration, and practical applications like machine learning. This course, suited for beginners to intermediate learners, will give you an understanding of b...Show More

TAKEAWAY SKILLS

Python 3

What You'll Learn

An understanding of the big data ecosystem, including data ingestion, integration methods, and big data storage options

A working knowledge of distributed computing fundamentals, covering parallel processing, partitioning strategies, and load balancing methodologies

The ability to utilize PySpark for diverse data operations, including processing, transformation, and analysis

Familiarity with basic and advanced data types, Spark SQL, machine learning algorithms, and data mining within PySpark

A working knowledge of PySpark's integration capabilities with various big data tools, such as Hadoop, Kafka, Hive, and others

What You'll Learn

An understanding of the big data ecosystem, including data ingestion, integration methods, and big data storage options

Course Content

Introduction to the Course

Get familiar with big data analysis using PySpark, covering ingestion, processing, and machine learning.

About This Course

What We Will Learn

Introduction to Big Data

Look at big data concepts, processing, storage solutions, and data ingestion strategies for analytics.

Overview of Big Data Big Data Processing Concepts Big Data Storage Concepts Introduction to Data Ingestion Quiz: Introduction to Big Data

Exploring PySpark Core and RDDs

Examine PySpark's architecture, core structures, and effective RDD operations for big data processing.

Introduction to Apache Spark Overview of PySpark Resilient Distributed Datasets (RDDs)RDD Operations Quiz: PySpark RDDs

PySpark DataFrames and SQL

Grasp the fundamentals of PySpark DataFrames, SQL operations, data exploration, and advanced data manipulation.

PySpark DataFrames Advanced PySpark DataFrame Operations Introduction to PySpark SQL Data Exploration and Analysis with PySpark SQL Quiz: PySpark Data Structures Solution: PySpark Data Structures

Customer Churn Analysis Using PySpark

Map out the steps for analyzing customer churn with PySpark, including preprocessing and exploratory data analysis.

Introduction to Customer Churn Analysis Quiz: Customer Churn Analysis Using PySpark Solution: Customer Churn Analysis Using PySpark

Machine Learning with PySpark

6 Lessons

Simplify complex machine learning concepts, PySpark MLlib, pipelines, and feature engineering.

Modeling with PySpark MLlib

5 Lessons

Piece together the parts of regression, classification, unsupervised learning, model tuning, and evaluation metrics in PySpark MLlib.

Predicting Diabetes in Patients Using PySpark MLlib

3 Lessons

Step through building and evaluating a diabetes prediction model using PySpark MLlib.

Performance Optimization in PySpark

5 Lessons

Unpack the core of optimizing PySpark performance using partitioning, broadcast variables, and DataFrame operations.

10.

PySpark Optimization: Analyzing NYC Restaurants Data

3 Lessons

Go hands-on with optimizing PySpark operations on NYC restaurant data for better performance.

11.

Integrating PySpark with Other Big Data Tools

4 Lessons

Grasp the fundamentals of integrating PySpark with key big data tools for scalable processing.

12.

Wrap Up

1 Lesson

Solve problems in big data using PySpark and other optimization strategies.

Apriori Algorithm for Finding Frequent Itemsets with PySpark

Project

Course Author

Trusted by 1.4 million developers working at companies

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

Evan Dunbar

ML Engineer

Carlos Matias La Borde

Software Developer

Souvik Kundu

Front-end Developer

Vinay Krishnaiah

Software Developer

Eric Downs

Musician/Entrepeneur

Kenan Eyvazov

DevOps Engineer

Anthony Walker

@_webarchitect_

Emma Bostian 🐞

@EmmaBostian

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