Converting Standalone Code to Distributed Code for Analytics

Course Title: Distributed Python Analytics with Spark and Grid Systems

Course Description:

This practical course is designed for data engineers, analysts, and developers who want to scale standalone Python analytics code into distributed environments. You’ll learn how to deploy PySpark applications, automate workflows with shell scripts, manage virtual environments, and work with grid computing tools—all with a focus on real-world use cases and error handling.

Course Outline:

Introduction

• Overview of converting standalone Python code to distributed analytics pipelines.

Spark Submit and PySpark Drivers

• Using spark-submit and understanding different submission types.
• Building and organizing PySpark applications.
• Tracking jobs with runid for monitoring and debugging.
• Managing memory and space on edge nodes for optimal performance.
• Linking Python driver files with YAML for dynamic configuration.

Shell Scripting for Job Automation

• Writing shell scripts to orchestrate distributed job runs.
• Managing permissions and converting scripts between environments.
• Creating a master shell script to run Spark jobs and invoke Python code.

Managing Python Virtual Environments (venv)

• Creating and configuring virtual environments.
• Deciding where to store venvs (/tmp vs. persistent folders).
• Activating environments and establishing handshakes between components.
• Setting environment variables for distributed workflows.
• Handling dependencies through requirements.txt.

Main Python Code Development

• Converting Jupyter notebooks into Python driver scripts for production.

Understanding Architecture Constraints

• Discussing limitations of current master-slave grid architectures and non-grid drivers.

Working with YAML Configuration

• Organizing and referencing YAML files for job and data location configurations.

Using Grid Tools and Commands

• Executing and managing distributed jobs with grid-specific command-line tools.

Troubleshooting and Error Handling

• Fixing Hadoop permission errors and managing access.
• Identifying and resolving missing keys, inputs, and common failures.