Distributed Tasks Demystified With Celery, Sqs & Python

What you'll learn
Fundamentals of multithreading in python
How to implement distributed tasks with Python & Django
Implement message passing communication between processes to build parallel applications
How to scale on the cloud with AWS Simple Queue Service (SQS)
Learn how to build a distributed social media data ingestor
Requirements
Have access to the internet
Elementary understanding of any programming language
Sublime Anaconda Python IDE or or any python IDE installed
Your enthusiasm to learn how to scale distributed applications
Description
This course teaches beginners to industry professionals the fundamental concepts of Distributed Programming in the context of python & Django. We look at how to build applications that increase throughput and reduce latency. In this course, we will take a dive intially in the irst part of the course and build a strong foundation of asynchronous parallel tasks using python-celery a distributed task queue framework. We will explore AWS SQS for scaling our parallel tasks on the cloud. These fundamentals will aid you in building scalable Python solutions for virtually any python project. By the end of this course, you will have learnt how to use popular distributed programming frameworks for python and Django. Through concepts learnt, you will discover the world of distributed computing with Python and how easy it is to build distributed components into your python or Django projects.
Why take this course?
As to be python developer or Django web application developer, Its important to learn how to build applications that are able to process long running jobs or tasks in a non blocking way e.g sending mass email, map reduce, running high computational functions. Building a chatting applications.This course builds up your skills in distribute programming giving you the tools you need to scale your applications
This course consist of projects you can use to implement in your real world projects with the sole emphasis of allowing your applications to become distributed and have asynchronous components
During the course, you will have online access to the instructor and get individualized answers to your questions posted on forums.As an enrolled student you will get lifetime access to over 30 lectures plus and counting including future series of lessonsThis course comes with a 30 day money back guarantee! This means If you are not satisfied in any way, you'll get your money back. Though you may just miss out of the future existing lessons planned ahead and free source code for your own personal or business projects. So what are you waiting for?
"Come join me and lets build distributed Python apps in a way that will advance your career, enhance your knowledge and potiential to earn even more. The best part is this doesnt need to be hard it actually can be fun"
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Prepping up your environment
Section 2: Getting started with Multithreading in Python
Lecture 3 Blocking vs non blocking (part 1)
Lecture 4 Blocking vs non blocking (part 2)
Lecture 5 Concurrency Consumer & Producer problem a deep dive
Lecture 6 Solving Consumer producers problem with Mutual Exlusion
Lecture 7 Controlling threads with conditions (Part 1)
Lecture 8 Controlling threads with conditions (Part 2)
Lecture 9 Controlling threads with conditions (Part 3)
Lecture 10 Daemon threads by example (Part 4)
Lecture 11 Consumer producer a thread safe FIFO queue
Section 3: Core Celery Distributed Tasks
Lecture 12 Getting started with Celery
Lecture 13 Celery backends & Asyncresult by example
Lecture 14 Python exception handling back to the basics
Lecture 15 Exception handling in Celery Explained
Lecture 16 Celery scheduled periodic tasks (Part 1)
Lecture 17 Celery scheduled periodic tasks (Part 2)
Lecture 18 Celery scheduled periodic tasks How to apply Mutex (Part 3)
Lecture 19 Celery scheduled periodic tasks solar schedules
Section 4: Distributed tasks with AWS SQS
Lecture 20 Introduction to distributed tasks with AWS SQS
Lecture 21 Creating your first AWS SQS Queue with your AWS Console
Lecture 22 How to create a AWS SQS background worker in python (Part 1)
Lecture 23 How to create a AWS SQS background worker in python (Part 2)
Lecture 24 Dead-letter Queues the theory
Lecture 25 Dead-letter Queues illustrated
Lecture 26 How to bypass AWS SQS (Simple Queue Service) 256kb payload limit
Section 5: Distributed data ingestor Project #1
Lecture 27 Introduction Project #1
Lecture 28 Real world examples of data ingestors
Lecture 29 Creating a twitter developer application and Authentication Token
Lecture 30 Building your first social ingestor twitter (Part 1)
Lecture 31 Building your first social ingestor twitter (Part 2)
Lecture 32 Building your first social ingestor twitter Rate Limits (Part 3)
Lecture 33 Building your first social ingestor twitter Handle (Part 4)
Lecture 34 Building your first social ingestor twitter Handle (Part 5)
Section 6: Distributed Email Workers Project #2
Lecture 35 Basic fundamentals of SMTP and transactional email Services
Lecture 36 Creating your first background email worker (Part 1)
Lecture 37 Creating your first background email worker (Part 2)
Lecture 38 Creating your first background email worker (Part 3)
Section 7: Python Development Tools
Lecture 39 Quick start guide: Getting started with PyCharm IDE (Mac)
Complete beginners to python programming language,Non Python programmers looking to explore distributed programming in python,Aspiring Technical Founders looking to implement distributed applications,Anyone wishing to learn how distributed programming works in python,Anyone wishing to implement asynchronous programming in python web applications

Homepage

https://www.udemy.com/course/distributed-tasks-demystified-with-celery-python/

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