Introduction to PySpark | CodeSignal Learn

intermediate

Introduction to PySpark

Data

4 courses

77 practices

10 hours

Dive into the world of Big Data with PySpark, combining the power of Python and Spark's distributed computing. Master RDDs, DataFrames, SQL operations, and MLlib essentials. Acquire practical skills in data manipulation and machine learning, paving your path as a powerful data engineer.

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252 learners

Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Big Data Processing

INTERMEDIATE

SQL and NoSQL Data Querying

INTERMEDIATE

Data Ingestion and Extraction

Tools you'll use

Python

Spark

Trusted by learners working at top companies

Course 1

Getting Started with PySpark and RDDs

5 lessons

22 practices

Embark on your PySpark adventure by mastering Resilient Distributed Datasets (RDDs). Create and transform data efficiently, unlocking the basics needed to handle large datasets and set the stage for exciting data processing challenges ahead.

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Course 2

Working with DataFrames in PySpark

5 lessons

22 practices

Unlock the dynamic world of PySpark DataFrames for advanced data manipulation. Master creation from various formats, and execute complex operations like filtering, joins, and handling missing data, scaling your ability to manage large datasets effectively.

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Course 3

Performing SQL Operations with PySpark

4 lessons

16 practices

Master the blend of SQL with PySpark to run complex queries and joins. Utilize User Defined Functions to enhance functionality, empowering you to extract meaningful insights from your data analysis workflow with ease and precision.

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Course 4

Navigating PySpark MLlib Essentials

4 lessons

17 practices

Explore PySpark MLlib and develop essential machine learning skills. Prepare datasets, train models, make predictions, and evaluate performance, gaining confidence in deploying models with PySpark's powerful MLlib capabilities.

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