Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Converting Standalone Code to Distributed Code for Analytics

Name: Converting Standalone Code to Distributed Code for Analytics
Rating: 5.0 (2 reviews)

Interview Prep for Deploying Python Analytics at Scale with PySpark and Grid Computing

Created byShivgan Joshi

Last updated 7/2025

English

What you'll learn

Deploy PySpark at Scale
Automate Distributed Workflows
Manage Dependencies in Distributed Environments
Troubleshoot Production Systems

Course content

10 sections • 41 lectures • 1h 7m total length

Why we need this course0:32
Introduction to Converting Standalone Code to Distributed Code for Analytics0:59
Video Intro to course jupyter notebook to spark shell submit2:25
Learn to convert standalone Jupyter notebooks into distributed spark submit workflows, swapping PySpark code, understanding Spark architecture, driver and executor roles, and wrappers for grid execution.
Introduction Part 23:50
Mimic a PySpark workflow from a Jupyter notebook by converting code into a driver.py, wrapping with shell scripts and YAML, and testing a distributed Spark Submit setup.
Transitioning Code from Production to Sandbox to Destination: A Logical Workflow2:15
Transitioning Code from Production to Sandbox to Destination: A Logical Workflow3:38
Execute an incremental migration from production to sandbox to destination by preserving permissions, mirroring folders, and converting notebooks to JavaScript, with post-migration validation via HDFS commands.
Structured Migration Framework1:32
Structured Migration Framework Prod Sandbox Destination4:15
Use a structured migration framework to move production code to sandbox and destination. Employ spark submit, environment setup, and incremental changes with validation and logs.
Lessons Learned2:10
Read logs to identify file, import function, or key errors, then incrementally align sister files, destination yaml, and sandbox setups to move from notebooks to spark submit for distributed analytics.
How to use the course1:02
Learn how to use the course to perform your job effectively, focus on videos and the quiz, and use the written section with commands to identify errors.

Spark Submit and different types0:43
Pyspark0:58
Tracking Job using runid0:04
Memory and Space Considerations on edge node1:19
Python or shell driver file pointing to YAML1:05
Placeholder1:56
Run the simplest code to validate the setup, trace asset files including rtf and py dependencies, and move only essential files into a minimal sandbox with correct asset placement.
Common Errors while running the sh code for spark submit2:03
Identify common errors when submitting final simulation code with a shell script to spark submit, production to sandbox. Address virtual environment, export commands, and source folder locations for Python files.
Fixing errors paths after moving files like sh venv and permissions1:44
Diagnose and fix path and permission errors when migrating to distributed analytics, checking shell scripts, Python files, environment variables, and temp logs, and validating virtual environment usage.
Case study Fixing Path case study post moving to Sandbox2:20
migrate code to the sandbox, fix import path and slash issues, correct CSV locations in YAML, and highlight avoiding hard coded input/output locations in this sandbox case study.
Unhooking and Hooking the new code in Implementation1:26
Unhook and hook the new code in the modeling workflow, starting with one model, and align notebooks or scripts through pipelines to enable information flow between production and sandbox.
Unhooking and Hooking the new code in Implementation part 21:16
Unhooking and hooking the new code requires extensive yaml changes, searching for keys with ctrl-shift-f in PyCharm, and building a parallel file system by adding a suffix during migration.
Debugging failed spark submit runs1:50
Diagnose and fix failed spark submit runs by inspecting logs, activating the grid environment, correcting the YAML file, adjusting HDFS paths, and removing hard coded values in driver.py.
Comparing the csv from notebook vs spark submit run2:19
Compare notebook and spark submit csv outputs by coercing pandas frames to floats, removing non-float columns, and using diff equals with join-based checks.

Venv Creation for spark submit0:42
Location - where to place in tmp or folder0:49
vevn Activation and Handshaking or deploy or broadcast on slaves1:13
Env variables1:02
Common issues with venv2:45
Explore venv issues when running notebooks and spark submit, including environment activation, handshakes between R, PySpark, grid, and proxy setup, plus creating fresh environments to resolve version mismatches.

Permissions on hadoop to write on hdfs2:03
Missing keys and missing inputs in python files0:04
Iteration to fix the errors sandbox after copying prod code1:34
Iterate to fix sandboxed errors after copying prod code to a sandbox environment, preserving both environments, adjusting imports and pipelines, and creating sandbox-specific version files for analytics deployment.
After successful Migration what to do - how to check outputs and make notes1:45
Verify sandbox and destination outputs, document point-by-point data flow in wiki notes from shell scripts to Python and YAML, then validate HDFS outputs with spark submit.
Wiki Notes Organization1:47
Organize a five-page wiki on converting standalone code to distributed analytics, detailing running steps, virtual environments, and common errors for sandbox versus destination.

Requirements

Description

Course Title: Interview Prep for 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.

Who this course is for:

Begineers

Converting Standalone Code to Distributed Code for Analytics

What you'll learn

Explore related topics

Course content

Introduction10 lectures • 23min

Spark Submit and Pyspark Python Codes Driver13 lectures • 19min

Shell Scripting3 lectures • 3min

Venv5 lectures • 7min

Moving Prod to Sandbox ultimately to Destination1 lecture • 3min

Main Python Code1 lecture • 2min

Limitation of Master vs Slave Architecture1 lecture • 2min

YAML Files1 lecture • 1min

Grid Management tools and grid commands1 lecture • 2min

Common Errors5 lectures • 7min

Requirements

Description

Who this course is for: