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Distributed Tasks Demystified with Celery, SQS & Python

4h 27m 50s
English
Paid

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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#1: Introduction
All Course Lessons (39)
#Lesson TitleDurationAccess
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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