Distributed Tasks Demystified with Celery, SQS & Python

Name: Distributed Tasks Demystified with Celery, SQS & Python
Price: 12.75 USD
Availability: InStock

4h 27m 50s

English

Paid

September 12, 2024

Unlock the power of distributed programming with our comprehensive course, tailored for everyone from beginners to seasoned industry professionals. Learn how to boost application throughput and minimize latency by mastering the art of building sophisticated applications using Python and Django.

Course Overview

Dive into the critical concepts of asynchronous parallel tasks, the backbone of distributed programming. The course begins by strengthening your foundational knowledge using Python-Celery, a powerful distributed task queue framework. We then transition to exploring AWS SQS, equipping you with the skills to efficiently scale parallel tasks in the cloud.

What You'll Learn

Understand the core principles of distributed programming within the Python ecosystem.
Build and manage asynchronous tasks using Python-Celery.
Scale your applications with AWS SQS to handle parallel computing efficiently.
Implement distributed components into your Python and Django projects seamlessly.

Course Outcomes

By the end of this course, you will be adept in using popular distributed programming frameworks specifically for Python and Django. The skills you gain will empower you to create scalable solutions for any Python project. Experience firsthand the ease of integrating distributed computing strategies into your work, and take your projects to new heights with confidence and effectiveness.

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Watch Online 39 lessons

0:00

#1: Introduction

All Course Lessons (39)

#	Lesson Title	Duration
1	Introduction Demo	05:13
2	Prepping up your environment	07:24
3	Blocking vs non blocking (part 1)	06:11
4	Blocking vs non blocking (part 2)	05:01
5	Concurrency Consumer & Producer problem a deep dive	06:38
6	Solving Consumer producers problem with Mutual Exlusion	06:19
7	Controlling threads with conditions (Part 1)	02:24
8	Controlling threads with conditions (Part 2)	08:17
9	Controlling threads with conditions (Part 3)	03:51
10	Daemon threads by example (Part 4)	02:06
11	Consumer producer a thread safe FIFO queue	05:57
12	Getting started with Celery	05:53
13	Celery backends & Asyncresult by example	08:45
14	Python exception handling back to the basics	13:43
15	Exception handling in Celery Explained	09:24
16	Celery scheduled periodic tasks (Part 1)	04:45
17	Celery scheduled periodic tasks (Part 2)	04:43
18	Celery scheduled periodic tasks How to apply Mutex (Part 3)	10:39
19	Celery scheduled periodic tasks solar schedules	01:21
20	Introduction to distributed tasks with AWS SQS	14:00
21	Creating your first AWS SQS Queue with your AWS Console	05:21
22	How to create a AWS SQS background worker in python (Part 1)	08:04
23	How to create a AWS SQS background worker in python (Part 2)	09:43
24	Dead-letter Queues the theory	07:12
25	Dead-letter Queues illustrated	10:17
26	How to bypass AWS SQS (Simple Queue Service) 256kb payload limit	10:33
27	Introduction Project #1	01:05
28	Real world examples of data ingestors	04:00
29	Creating a twitter developer application and Authentication Token	06:17
30	Building your first social ingestor twitter (Part 1)	01:23
31	Building your first social ingestor twitter (Part 2)	03:35
32	Building your first social ingestor twitter Rate Limits (Part 3)	08:53
33	Building your first social ingestor twitter Handle (Part 4)	12:11
34	Building your first social ingestor twitter Handle (Part 5)	07:53
35	Basic fundamentals of SMTP and transactional email Services	04:21
36	Creating your first background email worker (Part 1)	11:49
37	Creating your first background email worker (Part 2)	11:23
38	Creating your first background email worker (Part 3)	03:21
39	Quick start guide: Getting started with PyCharm IDE (Mac)	07:55

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