HomeCoursesMastering Big Data with PySpark

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Updated 4 months ago

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.

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Overview

Content

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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 big data tools and techniques. After completing this course, you’ll be fully equipped with effective problem-solving capabilities in real-world scenarios.

This course explores the big data ecosystem, focusing on hands-on utilization of PySpark—the Python API for Apache Spark. In t...Show More

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

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

TAKEAWAY SKILLS

Python 3

Content

48 Lessons5 Quizzes

Introduction to the Course

2 Lessons

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

5 Lessons

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

5 Lessons

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

6 Lessons

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

3 Lessons

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.

Overview of Machine Learning Concepts

Introduction to PySpark MLlib

The ML Pipeline in PySpark

Feature Extraction Techniques in PySpark MLlib

Feature Transformation Techniques in PySpark MLlib

Quiz: Machine Learning with PySpark

Modeling with PySpark MLlib

5 Lessons

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

Regression with PySpark MLlib

Classification with PySpark MLlib

Unsupervised Learning with PySpark MLlib

Model Selection and Hyperparameter Tuning Using PySpark MLlib

Model Evaluation Metrics in Depth

Predicting Diabetes in Patients Using PySpark MLlib

3 Lessons

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

Introduction to the Diabetes Prediction Analysis

Quiz: Predicting Diabetes Using PySpark MLlib

Solution: Predicting Diabetes Using PySpark MLlib

Performance Optimization in PySpark

5 Lessons

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

Introduction to Performance Optimization

Partition Optimization

Broadcast Variables and PySpark Accumulators

Optimizing DataFrame Operations in PySpark

Quiz: Performance Optimization in PySpark

10.

PySpark Optimization: Analyzing NYC Restaurants Data

3 Lessons

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

Introduction to PySpark Optimization Using NYC Restaurant Data

Quiz: Optimizing PySpark DataFrame Operations

Solution: Optimizing PySpark DataFrame Operations

11.

Integrating PySpark with Other Big Data Tools

4 Lessons

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

Integrating PySpark with Other Tools and Best Practices

PySpark Integration with Apache Hive

PySpark Integration with Apache Kafka

Quiz: PySpark Integration Tools

12.

Wrap Up

1 Lessons

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

Final Conclusion

Apriori Algorithm for Finding Frequent Itemsets with PySpark

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

Upendra Kumar Devisetty

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