Software Architecture with Python
(SOFTWARE-ARC-PYTHON.AJ1) / ISBN : 978-1-64459-218-2
Über diesen Kurs
Nutzen Sie den Kurs und das Labor „Softwarearchitektur mit Python“, um zu erfahren, wie Python in eine Anwendungsarchitektur passt. Das Labor ist Cloud-basiert, gerätefähig und kann problemlos in ein LMS integriert werden. Die Softwarearchitektur-Schulung hilft Ihnen, die verschiedenen architektonischen Qualitätsanforderungen zu verstehen, um ein Produkt zu entwickeln, das den Geschäftsanforderungen entspricht. Der Kurs vermittelt außerdem die Kenntnisse und Fähigkeiten, die für die Arbeit mit verschiedenen Techniken wie der Einbindung von DevOps, Continuous Integration und mehr erforderlich sind, um Ihre Anwendung robust zu machen.
Fähigkeiten, die Sie erwerben werden
Holen Sie sich die Unterstützung, die Sie brauchen. Melden Sie sich für unseren Kurs mit Lehrer an.
Unterricht
13+ Unterricht | 150+ Tests | 174+ Karteikarten | 174+ Glossar der Begriffe
Testvorbereitung
71+ Fragen vor der Beurteilung | 85+ Fragen nach der Bewertung |
Praktische Übungen
27+ LiveLab | 00+ Minutes
Preface
- What this course covers
- Conventions
Principles of Software Architecture
- Defining software architecture
- Characteristics of software architecture
- Importance of software architecture
- System versus enterprise architecture
- Architectural quality attributes
- Summary
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
Testability – Writing Testable Code
- Understanding testability
- White-box testing principles
- Summary
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
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
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
Design Patterns in Python
- Design patterns – elements
- Categories of design patterns
- Patterns in Python – creational
- Patterns in Python – structural
- Patterns in Python – behavioral
- Summary
Python – Architectural Patterns
- Introducing MVC
- Event-driven programming
- Microservice architecture
- Pipe and Filter architectures
- Summary
Deploying Python Applications
- Deployability
- Tiers of software deployment architecture
- Software deployment in Python
- Deployment – patterns and best practices
- Summary
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
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
Testability – Writing Testable Code
- Measuring Code Coverage
- Unit Testing a Module
- Using Test-Driven Development
- Unit Testing Using doctest
Good Performance is Rewarding!
- Measuring the Performance of Code Using timeit
- Collecting and Reporting Statistics
- Profiling with cProfile
- Implementing an LRU Cache Dictionary
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
Security – Writing Secure Code
- Serializing an object using code jail
- Making the Code Secure for Input
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