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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.
4.4
48 Lessons
12h
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- 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
Learning Roadmap
1.
Introduction to the Course
Introduction to the Course
Get familiar with big data analysis using PySpark, covering ingestion, processing, and machine learning.
2.
Introduction to Big Data
Introduction to Big Data
Look at big data concepts, processing, storage solutions, and data ingestion strategies for analytics.
3.
Exploring PySpark Core and RDDs
Exploring PySpark Core and RDDs
5 Lessons
5 Lessons
Examine PySpark's architecture, core structures, and effective RDD operations for big data processing.
4.
PySpark DataFrames and SQL
PySpark DataFrames and SQL
6 Lessons
6 Lessons
Grasp the fundamentals of PySpark DataFrames, SQL operations, data exploration, and advanced data manipulation.
5.
Customer Churn Analysis Using PySpark
Customer Churn Analysis Using PySpark
3 Lessons
3 Lessons
Map out the steps for analyzing customer churn with PySpark, including preprocessing and exploratory data analysis.
6.
Machine Learning with PySpark
Machine Learning with PySpark
6 Lessons
6 Lessons
Simplify complex machine learning concepts, PySpark MLlib, pipelines, and feature engineering.
7.
Modeling with PySpark MLlib
Modeling with PySpark MLlib
5 Lessons
5 Lessons
Piece together the parts of regression, classification, unsupervised learning, model tuning, and evaluation metrics in PySpark MLlib.
8.
Predicting Diabetes in Patients Using PySpark MLlib
Predicting Diabetes in Patients Using PySpark MLlib
3 Lessons
3 Lessons
Step through building and evaluating a diabetes prediction model using PySpark MLlib.
9.
Performance Optimization in PySpark
Performance Optimization in PySpark
5 Lessons
5 Lessons
Unpack the core of optimizing PySpark performance using partitioning, broadcast variables, and DataFrame operations.
10.
PySpark Optimization: Analyzing NYC Restaurants Data
PySpark Optimization: Analyzing NYC Restaurants Data
3 Lessons
3 Lessons
Go hands-on with optimizing PySpark operations on NYC restaurant data for better performance.
11.
Integrating PySpark with Other Big Data Tools
Integrating PySpark with Other Big Data Tools
4 Lessons
4 Lessons
Grasp the fundamentals of integrating PySpark with key big data tools for scalable processing.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
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.
ABOUT THE AUTHOR
Upendra Kumar Devisetty
A wet-lab molecular biology scientist turned bioinformatics expert and head of Data Science at Greenlight Biosciences. Author of Deep Learning for Genomics book and Big Data fundamentals via PySpark at Datacamp.
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