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.
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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.
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 lessons
- This 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"
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What's inside
Learning objectives
- 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
- 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
Syllabus
Introduction
In this lesson we cover all the packages that we need to get started with the course and prep up our development workspace.
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Syllabus
In this lesson we cover all the packages that we need to get started with the course and prep up our development workspace.
In this lesson we will discusses thread synchronization mechanisms in Python and how to synchronize access to shared resources, We take the consumer & producer problem from a different angle with conditions
In this lesson, we will touch base on a key important aspect of incorporating locks in distributed workers. Distributed locks are a useful primitive in many environments where different processes must operate on shared resources in a mutually exclusive way.
In this lesson we learn how to configure AWS simple queue service for distributed applications
PENDING LAUNCH 28/10/2017
In this lesson we take a look at what are dead letter queues and how they fit in distributed services with sqs
In this lesson we see a live example of how to implement dead letter queues in AWS SQS
In this special video we take a look at how to bypass AWS SQS 256kb hard limit when sending large data via SQS queues.
With the advent of numerous Social media platforms, the need has become of real importance to have the ability to tap into these platforms for their data. Such data allows businesses to understand the wants and needs of customers. Data extraction poses a fantastic use case to explore how to build and orchestrate distributed processes to pull data from various social media platforms. In this series of data injestor project lessons, we explore how to build our own distributed data injestor that will allow us to aggregate raw data while taking all concepts learnt and implementing them into a real-world use case that has been implemented by large multi-million dollar cooperation’s.
Most APIs are subject to a limit on how many calls can be made per second (or minute, or other short time period), This is in order to protect servers from being overloaded and maintain high quality of service for other clients wishing to use a service. In This lesson we explore twitter rate limits and how to overcome them
In this lesson we dive into the core basics of how SMTP works and look at what are your options for transactional email services
In this series of lessons we take a look at how we can craft out our email background worker.
In this video we take a walk through getting started with pycharm IDE
Good to know
Save this course
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Activities
Review 'Distributed Systems for Dummies' by Judith Hurwitz, Robin Bloor, Marcia Kaufman
Show steps
Gain a foundational understanding of distributed systems concepts and their practical applications.
View
Cloud Computing For Dummies
on Amazon
Show steps
-
Read and summarize key chapters on distributed system architectures, communication, and fault tolerance.
-
Identify real-world examples of distributed systems.
Participate in a study group or online forum to discuss distributed programming topics
Show steps
Engage with peers to exchange ideas, ask questions, and reinforce your learning through discussion.
Show steps
-
Identify a study group or online forum focused on distributed programming.
-
Actively participate in discussions, sharing your knowledge and seeking insights from others.
Develop a flowchart to illustrate the flow of data in a distributed system
Show steps
Creating a flowchart helps visualize and understand how data flows in a distributed system.
Show steps
-
Define the boundaries of the distributed system.
-
Identify the main components involved in data flow.
-
Map out the interactions between components, including data exchange.
-
Use standard flowchart symbols to represent different processes.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Work through tutorials on advanced distributed programming techniques, such as message queuing and load balancing
Show steps
Expand your knowledge and master advanced aspects of distributed programming.
Show steps
-
Identify online tutorials or resources covering advanced distributed programming topics.
-
Follow the tutorials, completing exercises and implementing concepts in code.
Write small scripts based on real-life examples
Show steps
Practice writing scripts based on examples to reinforce your understanding of distributed programming concepts.
Show steps
-
Identify a real-life scenario where distributed programming can be applied.
-
Design a script that implements a solution to the scenario.
-
Write the script in Python, incorporating concepts learned in the course.
-
Test and debug the script to ensure it works as expected.
Contribute to an open-source project related to distributed programming
Show steps
Gain real-world experience and contribute to the community by working on a practical distributed programming project.
Show steps
-
Identify open-source projects that are relevant to your interests and skills.
-
Contact the project maintainers and express your interest in contributing.
Build a simple distributed web application using Celery and Python
Show steps
Hands-on project to apply your knowledge and skills in distributed programming, solidifying your understanding.
Show steps
-
Design the application architecture and define the functionality.
-
Develop the application using Python and Celery, implementing distributed tasks.
-
Configure and deploy the application to a cloud platform.
-
Test and evaluate the application's performance and scalability.
Develop a prototype for a distributed data processing pipeline
Show steps
Create a tangible deliverable that demonstrates your ability to design and implement a distributed system for data processing.
Show steps
-
Define the use case and data sources for the pipeline.
-
Design the pipeline architecture, including data ingestion, processing, and storage.
-
Implement the pipeline using Python and distributed programming frameworks.
Review 'Distributed Systems for Dummies' by Judith Hurwitz, Robin Bloor, Marcia Kaufman
Show steps
Gain a foundational understanding of distributed systems concepts and their practical applications.
View
Cloud Computing For Dummies
on Amazon
Show steps
- Read and summarize key chapters on distributed system architectures, communication, and fault tolerance.
- Identify real-world examples of distributed systems.
Participate in a study group or online forum to discuss distributed programming topics
Show steps
Engage with peers to exchange ideas, ask questions, and reinforce your learning through discussion.
Show steps
- Identify a study group or online forum focused on distributed programming.
- Actively participate in discussions, sharing your knowledge and seeking insights from others.
Develop a flowchart to illustrate the flow of data in a distributed system
Show steps
Creating a flowchart helps visualize and understand how data flows in a distributed system.
Show steps
- Define the boundaries of the distributed system.
- Identify the main components involved in data flow.
- Map out the interactions between components, including data exchange.
- Use standard flowchart symbols to represent different processes.
Work through tutorials on advanced distributed programming techniques, such as message queuing and load balancing
Show steps
Expand your knowledge and master advanced aspects of distributed programming.
Show steps
- Identify online tutorials or resources covering advanced distributed programming topics.
- Follow the tutorials, completing exercises and implementing concepts in code.
Write small scripts based on real-life examples
Show steps
Practice writing scripts based on examples to reinforce your understanding of distributed programming concepts.
Show steps
- Identify a real-life scenario where distributed programming can be applied.
- Design a script that implements a solution to the scenario.
- Write the script in Python, incorporating concepts learned in the course.
- Test and debug the script to ensure it works as expected.
Contribute to an open-source project related to distributed programming
Show steps
Gain real-world experience and contribute to the community by working on a practical distributed programming project.
Show steps
- Identify open-source projects that are relevant to your interests and skills.
- Contact the project maintainers and express your interest in contributing.
Build a simple distributed web application using Celery and Python
Show steps
Hands-on project to apply your knowledge and skills in distributed programming, solidifying your understanding.
Show steps
- Design the application architecture and define the functionality.
- Develop the application using Python and Celery, implementing distributed tasks.
- Configure and deploy the application to a cloud platform.
- Test and evaluate the application's performance and scalability.
Develop a prototype for a distributed data processing pipeline
Show steps
Create a tangible deliverable that demonstrates your ability to design and implement a distributed system for data processing.
Show steps
- Define the use case and data sources for the pipeline.
- Design the pipeline architecture, including data ingestion, processing, and storage.
- Implement the pipeline using Python and distributed programming frameworks.
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