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Software Architecture with Python

(SOFTWARE-ARC-PYTHON.AJ1) / ISBN : 978-1-64459-218-2
This course includes
Interactive Lessons
Gamified TestPrep
Hands-On Labs
AI Tutor (Add-on)
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About This Course

Use Software Architecture with Python course and lab to get to know how Python fits into an application architecture. The lab is cloud-based, device-enabled, and can easily be integrated with an LMS. The software architecture training will help you gain an understanding of the different architectural quality requirements to help build a product that satisfies business needs. The course also provides knowledge and skills required to work with various techniques such as incorporating DevOps, Continuous Integration, and more to make your application robust.

Skills You’ll Get

Download Course Outline

1

Preface

  • What this course covers
  • Conventions
2

Principles of Software Architecture

  • Defining software architecture
  • Characteristics of software architecture
  • Importance of software architecture
  • System versus enterprise architecture
  • Architectural quality attributes
  • Summary
3

Writing Modifiable and Readable Code

  • What is modifiability?
  • Aspects related to modifiability
  • Understanding readability
  • Fundamentals of modifiability – cohesion and coupling
  • Exploring strategies for modifiability
  • Metrics – tools for static analysis
  • Refactoring code
  • Summary
4

Testability – Writing Testable Code

  • Understanding testability
  • White-box testing principles
  • Summary
5

Good Performance is Rewarding!

  • What is performance?
  • Software performance engineering
  • Performance testing and measurement tools
  • Performance complexity
  • Measuring performance
  • Profiling
  • Other tools
  • Programming for performance – data structures
  • Summary
6

Writing Applications that Scale

  • Scalability and performance
  • Concurrency
  • Thumbnail generator
  • Multithreading – Python and GIL
  • Multithreading versus multiprocessing
  • Pre-emptive versus cooperative multitasking
  • The asyncio module in Python
  • Waiting for a future – async and await
  • Concurrent futures – high-level concurrent processing
  • Scaling for the web
  • Scaling workflows – message queues and task queues
  • Celery – a distributed task queue
  • Summary
7

Security – Writing Secure Code

  • Information security architecture
  • Secure coding
  • Common security vulnerabilities
  • Is Python secure?
  • Security issues with web applications
  • Strategies for security – Python
  • Secure coding strategies
  • Summary
8

Design Patterns in Python

  • Design patterns – elements
  • Categories of design patterns
  • Patterns in Python – creational
  • Patterns in Python – structural
  • Patterns in Python – behavioral
  • Summary
9

Python – Architectural Patterns

  • Introducing MVC
  • Event-driven programming
  • Microservice architecture
  • Pipe and Filter architectures
  • Summary
10

Deploying Python Applications

  • Deployability
  • Tiers of software deployment architecture
  • Software deployment in Python
  • Deployment – patterns and best practices
  • Summary
11

Techniques for Debugging

  • Maximum subarray problem
  • Simple debugging tricks and techniques
  • Logging as a debugging technique
  • Debugging tools – using debuggers
  • Advanced debugging – tracing
  • Summary
-

Appendix - A

  • Installing Python
  • Running Python
  • Basic syntax
  • Conditional statements and loops
  • Data structures
  • Functions
  • Summary
-

Appendix - B

  • Object-oriented programming
  • Modules and packages
  • File operations
  • Error and exception handling
  • Summary

1

Writing Modifiable and Readable Code

  • Documenting the Code
  • Understanding the Concept of Cohesion
  • Finding the McCabe Metric
  • Running a Static Checker
  • Fixing Code Smells by Refactoring the Code
  • Fixing Code Complexity by Refactoring the Code
2

Testability – Writing Testable Code

  • Measuring Code Coverage
  • Unit Testing a Module
  • Using Test-Driven Development
  • Unit Testing Using doctest
3

Good Performance is Rewarding!

  • Measuring the Performance of Code Using timeit
  • Collecting and Reporting Statistics
  • Profiling with cProfile
  • Implementing an LRU Cache Dictionary
4

Writing Applications that Scale

  • Using the Multiprocessing Pool Object
  • Creating a Co-operative Multitasking Scheduler Using Simple Python Generators
  • Using the asyncio Module
  • Using async and await
  • Using the concurrent.futures Module
5

Security – Writing Secure Code

  • Serializing an object using code jail
  • Making the Code Secure for Input
6

Techniques for Debugging

  • Debugging Maximum Subarray Problem
  • Generating Random Patient Data Using the schematics Library
  • Debugging the Word Searcher Program
  • Creating a Log File Using Logger Objects
  • Creating a Simple Log File
  • Debugging with pdb

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