Apache Spark 3 for Data Engineering & Analytics with Python

Learn how to use Python and PySpark 3.0.1 for Data Engineering / Analytics (Databricks) - Beginner to Ninja

Apache Spark 3 for Data Engineering & Analytics with Python

InstructorDavid Charles Academy

Duration8h 40m

Students11.5K

Rating4.6 (2,128)

Comidoc Analysis

Spark Architecture and Databricks Workflows with Outdated Setup Instructions

Strengths

Core Technical Concepts

Covers essential Spark architecture, execution concepts including DAG visualization, and both RDD and Structured API transformations.

Best suited to

Python developers seeking Spark fundamentals
Aspiring data engineers needing Databricks exposure

Less suited to

Learners wanting a seamless, up-to-date installation experience
Those requiring highly precise instructional clarity for complex exercises

Editorial course preview

What the public course preview actually shows

These 4 complementary views highlight concrete, legible examples from the course presentation.

A flowchart diagram on a pink background showing four yellow modules: Introduction, Installation, Spark Execution, and RDD Crash Course, connected by orange arrows with topic lists — Preview 1 of 4
This course roadmap outlines the curriculum progression through four main sections: Introduction, Installation, Spark Execution, and RDD Crash Course, detailing specific topics for each stage.

A split-screen educational slide showing a Jupyter Notebook with Python code on the left and a data analysis problem statement about windmill air velocity statistics on the right. — Preview 2 of 4
This slide demonstrates calculating summary statistics for an energy company using Apache Spark in a Jupyter Notebook, alongside a dataset of daily air velocities.

A split-screen educational slide titled 'Calculate Summary Stats for an Energy Company'. The left side shows a Jupyter Notebook interface with Python code calculating mean, air var — Preview 3 of 4
This instructional slide demonstrates how to calculate summary statistics like variance and standard deviation using Python in a Jupyter Notebook for wind energy data analysis.

A presentation slide with a dark background featuring an abstract red ink pattern, displaying white text about Spark Architecture and Python application development, with logos for — Preview 4 of 4
This slide outlines the course objectives, focusing on gaining an in-depth understanding of Spark Architecture and learning to develop Spark Applications using Python.