Learn how to use Python and PySpark 3.0.1 for Data Engineering / Analytics (Databricks) - Beginner to Ninja
InstructorDavid Charles Academy
Coupon
Covers essential Spark architecture, execution concepts including DAG visualization, and both RDD and Structured API transformations.
Provides a path from cluster creation to building dashboards and using notebooks for data processing.
Editorial course preview
These 4 complementary views highlight concrete, legible examples from the course presentation.
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.
This slide demonstrates calculating summary statistics for an energy company using Apache Spark in a Jupyter Notebook, alongside a dataset of daily air velocities.
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.
This slide outlines the course objectives, focusing on gaining an in-depth understanding of Spark Architecture and learning to develop Spark Applications using Python.
Installation instructions for local environments may require external research to successfully complete due to aging documentation.
The Databricks Free edition differs from the Community edition shown in the lessons, and the web UI has undergone changes.
Some exercises may encounter errors or typos, and certain method use-cases are not always clearly defined.
Aligns with the goal of teaching Python developers and aspiring engineers how to use PySpark and Databricks.