Updated June 2026

Distributed Task Automation with Python Faust and Kafka

Class Duration

16 hours of live training delivered over 2-4 days.

Student Prerequisites

Proficiency in Python programming, including experience with Python 3.x.
Familiarity with basic concepts of distributed systems and task automation.
Experience with Docker and containerization concepts is beneficial but not required.
Basic understanding of message brokers and stream processing concepts is helpful but not required.
All students should have taken the Python Task Automation course or have significant experience with the topics covered in the Python Task Automation course.

Target Audience

Experienced Python developers and programming professionals ready to scale automation and streamline backend architecture by building resilient, real-time distributed task pipelines with Python Faust and Apache Kafka. This course builds on the fundamentals from Task Automation with Python.

Description

This course equips Python developers with practical expertise in distributed task automation using Python Faust and Apache Kafka, building resilient, high-performance pipelines with Kafka for messaging and Faust for stream processing. You'll start by exploring the fundamentals of task automation before diving into hands-on environment setup: installing Python tools, containerizing your applications with Docker, and standing up a Kafka cluster. From there, you'll master Faust's powerful real-time stream processing API, learning to manage state, ensure fault tolerance, and handle errors gracefully. We'll then show you how to monitor your applications, tune performance, and scale seamlessly in production, with best practices for deployment and observability. Along the way, we'll tailor examples to your domain so you leave with immediately applicable skills for streamlining backend architecture and automating complex workflows at scale.

Learning Outcomes

Understand the concept and application of Distributed Task Automation.
Set up and configure a Python development environment for script programming.
Learn the basics of containerization and how to use Docker for creating and running containers.
Gain in-depth knowledge of Kafka, its architecture, and how to set up a Kafka cluster.
Master the basics and advanced concepts of Python Faust, including agents, stream processing, state management, and fault tolerance.
Learn how to monitor and manage Kafka and Faust applications, including error handling and retry logic.
Understand the best practices for deploying Kafka and Faust in production, ensuring high availability and optimizing performance.
Implement a real-time data pipeline with Faust and Kafka.

Training Materials

Comprehensive courseware is distributed online at the start of class. All students receive a downloadable MP4 recording of the training.

Software Requirements

Students will need a free, personal GitHub account to access the courseware. Student machines will need a text editor like Visual Studio Code, the latest Python version, Docker Desktop, PanDoc, and OpenOffice. Students will need permission to install NPM and PyPi packages as well as the ability to download Docker images. Preconfigured student virtual machines can be provided upon request.

Training Topics

Overview of Distributed Task Automation

What is Distributed Task Automation?
Overview of Python Faust (the maintained faust-streaming fork; the original Robinhood Faust is no longer maintained)
Faust compared to Celery
What is Streaming?
What is Kafka?
What is KRaft? (Kafka 4.x runs without ZooKeeper)
Kafka compared to RabbitMQ + PostgreSQL

Development Environment

Configure Visual Studio Code for Python Script Programming
Python Code Linting & Reformatting with Ruff & MyPy
Debugging Python Scripts with Visual Studio Code
Docker Desktop

Containerization

What is a Container?
What is Docker?
What is Docker Hub?
Images and Containers
Create an Image with Dockerfile
Run Containers
Configure Containers with Environment Variables
Docker Compose
Docker Compose Networking
Docker Compose Volume

Scaling Faust Applications

Parallelism and Partitioning in Kafka
Running Multiple Faust Workers

Monitoring and Management

Monitoring Kafka and Faust
Using Kafka Monitoring Tools (e.g., Kafka UI, Confluent Control Center)
Logging and Metrics in Faust
Handling Errors and Retries
Configuring Error Handling in Faust
Implementing Retry Logic

Scaling and Deployment

Deploying Kafka and Faust in Production
Best Practices for Kafka Cluster Deployment
Deploying Faust Apps with Docker and Kubernetes
High Availability and Fault Tolerance
Configuring Kafka for High Availability
Ensuring High Availability in Faust Applications

Performance Tuning

Kafka Performance Tuning
Optimizing Faust Performance
Implementing a Real-Time Data Pipeline with Faust and Kafka