Python

Django

Pytest

It is the ability to define a system’s architecture, components, interfaces, and data to meet a set of functional and non-functional requirements. It's not just about knowing technologies, but about making design decisions based on trade-offs (cost, performance, scalability, maintainability, etc.) to solve a business problem in an effective and sustainable way.

Ultimately, it covers everything from high-level architectural decisions (such as defining the overall architecture to use, how to organize and communicate services, and how system components interact) to low-level, detailed design of sustainable and adaptable code that represents business concepts.

System Design

Persistence in system design refers to the capability of the system to store data in a manner that it survives power failures, crashes, or errors. It's a critical aspect of any system design.

Persistence

Observability refers to how well internal states of a system can be inferred from its external outputs. It's crucial for understanding system performance, identifying issues, and ensuring optimal operation.

Observability

Infrastructure refers to the fundamental physical and virtual resources supporting a system. It involves hardware, software, networks, and facilities necessary for system operation and management.

Infrastructure

Testing is a critical phase in the system design process where the system is evaluated to ensure it functions as intended, maintains performance under different conditions, and detects any flaws or errors.

Testing

Systems Engineer and Software Developer. Since the beginning of my career, I've always had a focus on Backend technologies, although I'm also comfortable as a Fullstack developer. Throughout my professional journey, I've worked with technologies such as Laravel, modern object-oriented PHP, Docker, MySQL, MongoDB, Redis, Doctrine, Blade, Vue, Typescript, NodeJS, React, and AWS.

David Alonso

Python is a high-level, versatile, and interpreted programming language known for its simplicity and readability. It emphasizes code readability through its clean and concise syntax, making it a popular choice for beginners and experienced developers alike. Python's extensive standard library and third-party packages support a wide range of applications, from web development and data analysis to artificial intelligence and scientific computing. Its interpreted nature allows for rapid development and cross-platform compatibility, making Python a versatile and widely adopted language in the world of programming

Serverless architecture allows developers to build and run applications without the need to manage servers, with infrastructure managed by the cloud provider.

Serverless Architecture

Microservices architecture is a software design style that breaks an application into small autonomous services, each running its own process and communicating through APIs.

Microservices Architecture

Clean code is a development practice focused on writing code that is easy to understand, read, and modify, with the aim of improving software quality and reducing complexity, facilitating maintainability and system evolution.

Clean Code

Relational databases use a structured, tabular format to store data, enabling complex queries and relationships. Examples include MySQL, PostgreSQL, and Oracle.

Relational Databases

Monolithic architecture is a software design approach where all application functions reside in a single instance or component, simplifying development and testing.

Monolithic Architecture

NoSQL databases, non-relational by design, provide high scalability and flexibility. They are ideal for handling large data sets and non-structured data. Examples include MongoDB, Cassandra, and CouchDB.

NoSQL Databases

Redis is an open-source, in-memory data structure store, used as a database, cache, and message broker. It supports various data structures and provides high performance.

Redis

MongoDB is a leading NoSQL database that stores data in flexible, JSON-like documents. It provides horizontal scalability and allows for querying and indexing of data.

MongoDB

Application monitoring involves tracking the performance and functionality of software applications. It aids in the early detection of issues, ensuring smooth and efficient application performance.

Application monitoring

Log management entails the process of generating, transmitting, storing, analyzing, and disposing of log data from different sources. It's essential for troubleshooting, security, compliance, and understanding user behavior.

Log management

Caching is a technique for storing data in temporary storage areas, or 'caches', to improve data retrieval speed and system performance, reducing the workload on primary storage resources.

Caching

Cloud providers offer services and resources for storing and accessing data and applications over the internet, allowing users to access their data from anywhere. They offer scalability, flexibility, and cost savings. Examples include Amazon Web Services, Microsoft Azure, and Google Cloud.

Cloud providers

Unit testing focuses on validating individual components, functions, or classes in isolation to ensure that each part of the system operates correctly and handles expected inputs and outputs.

Unit testing

Integration testing evaluates how well different components or modules of a system work together, ensuring seamless interaction and identifying issues that may arise from their integration.

Integration testing

Test doubles are objects used in testing to replace real components with controlled versions, allowing for better isolation, control, and predictability during the testing process.

Test doubles

This encompasses more complex Python topics such as object-oriented programming, decorators, generators, multithreading, metaclasses, and design patterns.

Advanced Concepts

Django is a high-level Python web framework. In this context, it's discussed Django's architecture, models, views, templates, ORM (Object-Relational Mapping), and its role in building web applications.

This category covers the core principles of Python programming, including data types, variables, control flow (if statements, loops), functions, and basic data structures like lists, dictionaries, and sets.

Fundamentals

This focuses on optimizing Python code for speed and efficiency. Topics include profiling, algorithmic complexity analysis, memory management, and techniques like caching, asynchronous programming, and parallel processing.

Antonio

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About me

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Software Developer