Django Celery Mastery: Python Asynchronous Task Processing

Django Celery Mastery: Python Asynchronous Task Processing, available at $79.99, has an average rating of 4.48, with 50 lectures, based on 230 reviews, and has 1965 subscribers.

You will learn about Module 1: You will learn how to set up a working environment for Django Celery by building Docker containers for Django, Redis, and Celery workers. Module 2: You will learn to create and register tasks within a Django application, start and manage Celery. Module 3: You will learn to identify common exceptions and errors in Celery tasks and implement error control. Module 4: You will learn to schedule tasks at specific times or intervals, customize periodic tasks, and utilize crontab schedules. By the end of the course, you will have a solid understanding of Django Celery and be equipped to leverage its power. This course is ideal for individuals who are Python Developers: Developers who are already familiar with the Python programming language and want to expand their knowledge and skills in asynchronous task processing using Django Celery. or Django Developers: Developers who are experienced in Django web development and want to incorporate asynchronous task processing into their Django applications to enhance performance and scalability. or Web Application Developers: Professionals involved in web application development who want to learn how to leverage Django Celery for handling time-consuming and resource-intensive tasks asynchronously, such as sending emails, generating reports, or processing large datasets. or Software Engineers: Software engineers interested in learning about task queues and asynchronous processing in Python and Django. or Backend Developers: Backend developers who want to improve the efficiency of their applications by offloading time-consuming tasks to background workers using Django Celery. or Technical Leads and Architects: Technical leads and architects responsible for designing and implementing scalable and performant systems who want to explore asynchronous task processing techniques using Django Celery. or Anyone who is interested and keen to learn Python Celery It is particularly useful for Python Developers: Developers who are already familiar with the Python programming language and want to expand their knowledge and skills in asynchronous task processing using Django Celery. or Django Developers: Developers who are experienced in Django web development and want to incorporate asynchronous task processing into their Django applications to enhance performance and scalability. or Web Application Developers: Professionals involved in web application development who want to learn how to leverage Django Celery for handling time-consuming and resource-intensive tasks asynchronously, such as sending emails, generating reports, or processing large datasets. or Software Engineers: Software engineers interested in learning about task queues and asynchronous processing in Python and Django. or Backend Developers: Backend developers who want to improve the efficiency of their applications by offloading time-consuming tasks to background workers using Django Celery. or Technical Leads and Architects: Technical leads and architects responsible for designing and implementing scalable and performant systems who want to explore asynchronous task processing techniques using Django Celery. or Anyone who is interested and keen to learn Python Celery.

Enroll now: Django Celery Mastery: Python Asynchronous Task Processing

Summary

Title: Django Celery Mastery: Python Asynchronous Task Processing

Price: $79.99

Average Rating: 4.48

Number of Lectures: 50

Number of Published Lectures: 50

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Module 1: You will learn how to set up a working environment for Django Celery by building Docker containers for Django, Redis, and Celery workers.
Module 2: You will learn to create and register tasks within a Django application, start and manage Celery.
Module 3: You will learn to identify common exceptions and errors in Celery tasks and implement error control.
Module 4: You will learn to schedule tasks at specific times or intervals, customize periodic tasks, and utilize crontab schedules.
By the end of the course, you will have a solid understanding of Django Celery and be equipped to leverage its power.

Who Should Attend

Python Developers: Developers who are already familiar with the Python programming language and want to expand their knowledge and skills in asynchronous task processing using Django Celery.
Django Developers: Developers who are experienced in Django web development and want to incorporate asynchronous task processing into their Django applications to enhance performance and scalability.
Web Application Developers: Professionals involved in web application development who want to learn how to leverage Django Celery for handling time-consuming and resource-intensive tasks asynchronously, such as sending emails, generating reports, or processing large datasets.
Software Engineers: Software engineers interested in learning about task queues and asynchronous processing in Python and Django.
Backend Developers: Backend developers who want to improve the efficiency of their applications by offloading time-consuming tasks to background workers using Django Celery.
Technical Leads and Architects: Technical leads and architects responsible for designing and implementing scalable and performant systems who want to explore asynchronous task processing techniques using Django Celery.
Anyone who is interested and keen to learn Python Celery

Target Audiences

Python Developers: Developers who are already familiar with the Python programming language and want to expand their knowledge and skills in asynchronous task processing using Django Celery.
Django Developers: Developers who are experienced in Django web development and want to incorporate asynchronous task processing into their Django applications to enhance performance and scalability.
Web Application Developers: Professionals involved in web application development who want to learn how to leverage Django Celery for handling time-consuming and resource-intensive tasks asynchronously, such as sending emails, generating reports, or processing large datasets.
Software Engineers: Software engineers interested in learning about task queues and asynchronous processing in Python and Django.
Backend Developers: Backend developers who want to improve the efficiency of their applications by offloading time-consuming tasks to background workers using Django Celery.
Technical Leads and Architects: Technical leads and architects responsible for designing and implementing scalable and performant systems who want to explore asynchronous task processing techniques using Django Celery.
Anyone who is interested and keen to learn Python Celery

In today’s fast-paced web development landscape, efficiently handling time-consuming and resource-intensive tasks is crucial for building high-performance applications. Django Celery, a powerful asynchronous task-processing library, provides the perfect solution to address this challenge. This comprehensive course, “Django Celery Mastery: Python Asynchronous Task Processing,” is designed to empower you with the knowledge and skills necessary to harness the full potential of Django Celery and elevate your Python web applications to new heights of scalability and responsiveness.

Course Description: The course begins by guiding you through the process of setting up a fully functional Django Celery working environment. You’ll learn the essentials of Django Celery, explore task producers and consumers, and gain hands-on experience building Docker containers for Django, Redis (the message broker), and Celery workers. Additionally, you’ll understand the role of a results backend and create a Redis Docker container to facilitate effective task communication.

Moving forward, you’ll dive deep into defining and executing Celery tasks within a Django application. You’ll discover how to create and register tasks, start and manage Celery workers, and configure task routing for optimized task distribution. Advanced concepts such as task prioritization, task grouping, task chaining, task rate limits, and passing arguments and returning results from Celery tasks will be thoroughly covered. You’ll also explore both synchronous and asynchronous task execution approaches and leverage the Flower monitoring tool to track and monitor Celery workers and tasks.

Handling task failures and retries is a critical aspect of asynchronous task processing, and this course provides comprehensive introduction of this topic. You’ll gain insights into common types of exceptions and errors in Celery tasks and explore various error handling strategies. You’ll implement automatic retries, handle errors in task groups and chains, and discover techniques for handling failed tasks and task timeouts. Additionally, you’ll learn how to gracefully shut down tasks, clean up failed tasks, and leverage error tracking and monitoring tools such as Sentry.

Task scheduling and periodic tasks play a vital role in managing recurring tasks efficiently. In this course, you’ll understand the fundamentals of task scheduling, including scheduling tasks to run at specific times or intervals. You’ll explore the customization of periodic tasks, implement crontab schedules, and ensure schedule persistence in a Django application. Furthermore, you’ll learn how to schedule Django custom commands using Celery Beat and monitor service status using custom event tracking and alerting mechanisms.

Throughout the course, hands-on exercises, practical examples, and real-world scenarios will enhance your learning experience and enable you to apply the concepts directly in your own projects. By the end of this course, you’ll have gained mastery over Django Celery and be equipped with the skills to implement efficient asynchronous task processing in Python applications, ensuring scalability, responsiveness, and optimal resource utilization.

Whether you are a Python developer, Django developer, web application developer, software engineer, backend developer, or a technical lead/architect, this course will empower you to unlock the full potential of Django Celery and revolutionize your approach to asynchronous task processing. Don’t miss this opportunity to level up your skills and supercharge your applications with the power of Celery. Enroll now and take the first step towards mastering asynchronous task processing in Python!

Legal Notice: “Celery Logo” is licensed under the Creative Commons Attribution-Share Alike 4.0 International license. To view a copy of this license, visit creativecommons org licenses by-sa 4.0. No changes were made to the original file. This course is not accredited, certified, affiliated with, or endorsed by Celery Project.

Trademark Usages and Fees Disclosures:

Usage of Django Logo: The Django logo used in this product is for identification purposes only, to signify that the content or service is Django-related. It does not imply that this product is officially endorsed by the Django Software Foundation (DSF) or the Django Core team as representatives of the Django project.

Fees Disclosure: We would like to clarify that 100% of the fees will be retained by the author to support the ongoing development and maintenance of this product. Currently, 0% of the fees, if applicable, will be contributed back to the DSF as a donation to further support the Django community.

Note: The Django name and logo are registered trademarks of the Django Software Foundation, and their usage is subject to the Django Trademark License Agreement.

Usage of Celery Logo: The Celery logo used in this product is for identification purposes only, to signify that the content or service is Celery-Project-related. It does not imply that this product is officially endorsed by the Celery Project or the logo licensor. Author Ty Wilkins – Licensed under the Creative Commons Attribution-Share Alike 4.0 International license.

Course Curriculum

Chapter 1: Source Code

Lecture 1: Introduction

Lecture 2: Code Examples

Chapter 2: Setup Guides

Lecture 1: [Windows] Installing Python

Lecture 2: [Windows] Visual Studio Code Induction

Lecture 3: [Windows] Creating Virtual Environments

Lecture 4: [macOS] Visual Studio Code Induction

Lecture 5: [macOS] Installing Python

Lecture 6: [macOS] Creating Virtual Environments

Lecture 7: Docker Installation

Chapter 3: Preparing a Django Celery Working Environment

Lecture 1: Introducing Django Celery

Lecture 2: Introducing Task Producers

Lecture 3: Building a Django Docker Container

Lecture 4: Building a Redis Docker Container

Lecture 5: Introducing Task consumers (Workers)

Lecture 6: Building a Celery Worker Docker Container

Lecture 7: Introducing Results Backend

Chapter 4: Defining and Executing Celery Tasks

Lecture 1: Creating and Registering Celery Tasks in Django

Lecture 2: Starting the Celery Worker

Lecture 3: Initiating a Celery Task

Lecture 4: Creating a new standalone Celery Worker

Lecture 5: Introducing Tasks Routing

Lecture 6: Configuring Task Routing

Lecture 7: Introducing Celery Task Prioritization

Lecture 8: Configuring Task Prioritization (Redis)

Lecture 9: The Primitives – Task Grouping

Lecture 10: The Primitives – Task Chaining

Lecture 11: Task Rate limits

Lecture 12: Configuring Task Prioritization (RabbitMQ)

Lecture 13: Passing arguments and returning results from Celery tasks

Lecture 14: Executing tasks synchronously and asynchronously

Lecture 15: Monitoring Celery Workers and Tasks with Flower

Chapter 5: Handling Task Failures and Retries

Lecture 1: Common Types of Exceptions and Errors in Celery Tasks

Lecture 2: Dynamic Task Discovery in Celery: Auto-discovering Tasks in a Directory

Lecture 3: Error Handling: Try Except Blocks

Lecture 4: Handling Errors in Celery Tasks with Custom Task Classes

Lecture 5: Implementing Automatic Retries

Lecture 6: Error Handling in Groups

Lecture 7: Towards Error Handling in Task Chains

Lecture 8: Towards Dead-letter Queues: Handling Failed Tasks

Lecture 9: Task Timeouts and Task Revoking (Using task time limits and timeouts)

Lecture 10: Handling Errors in Task Result Callbacks

Lecture 11: Task Signals Graceful Shutdown and Cleanup of Failed Tasks

Lecture 12: Error Tracking and Monitoring with Sentry

Chapter 6: Task Scheduling and Periodic Tasks

Lecture 1: Introduction to Task Scheduling

Lecture 2: Scheduling Tasks to Run at Specific Times or Intervals

Lecture 3: Implementing Periodic Tasks Customization

Lecture 4: Crontab Schedules

Lecture 5: Implement Schedule Persistence for Celery in a Django Application

Lecture 6: Schedule a Django Custom Command with Celery Beat

Lecture 7: Monitoring Service Status Including Custom Event Tracking and Alerting

Instructors

Very Academy
Technical Education

Rating Distribution

1 stars: 7 votes
2 stars: 7 votes
3 stars: 20 votes
4 stars: 63 votes
5 stars: 133 votes

Frequently Asked Questions

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