10-15-2024, 12:47 PM
The Complete Python Microservices
![[Image: e79009016639ad196fab1b5c66166ac8.jpeg]](https://i124.fastpic.org/big/2024/1015/c8/e79009016639ad196fab1b5c66166ac8.jpeg)
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 942.54 MB | Duration: 0h 56m
You will learn the best practices of Python Microservices and learn how to build Python Microservices!
What you'll learn
Learn everything about Python Microservices
Learn how to build the Python Microservices
Learn python development, deployment, scaling, and management
Learn Python and best practices of Python microservices
Requirements
You need to be interested in learning Python Microservices
Description
Python Microservices are an architectural style that structures an application as a collection of loosely coupled services, each of which represents a specific business capability. In Python, microservices have gained popularity due to the language's simplicity, readability, and vast ecosystem of libraries. Each microservice operates independently and communicates with other services over a network, typically through HTTP or messaging protocols. Python has libraries such as Prometheus for monitoring and OpenTelemetry for tracing, which help developers track the health and performance of their services.Python microservices architecture, each service is developed, deployed, and maintained independently. This isolation allows teams to work on different services without worrying about breaking other parts of the system. The microservices paradigm contrasts with monolithic applications, where all functionality is built into a single, large codebase. One key advantage of microservices is scalability. Services can be scaled individually based on their specific load requirements rather than scaling the entire application, as is often necessary with monoliths. Flask and FastAPI are two popular lightweight web frameworks used to build RESTful APIs for microservices. Python's simplicity, wide library support, and ease of integration with modern cloud-native tools make it an excellent choice for building microservices architectures. Python supports several methods, including synchronous HTTP requests and asynchronous message queues like RabbitMQ and Kafka.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Data Visualization and Chats in Python Microservices
Lecture 3 Methods in programming language
Lecture 4 Matplotlib in Python Microservices
Lecture 5 Pandas Dataframe in Python Microservices
Lecture 6 Python lists to Nampy in Python Microservices
Lecture 7 Operating on Nampy arrays in Python Microservices
Lecture 8 Retrieving data frim a data frame
This course is for those wanting to learn Python Microservices
Screenshots
![[Image: ab168e0925a058f307d80b6813168996.jpeg]](https://i124.fastpic.org/big/2024/1015/96/ab168e0925a058f307d80b6813168996.jpeg)
Say "Thank You"
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ddownload.com:
Published 10/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 942.54 MB | Duration: 0h 56m
You will learn the best practices of Python Microservices and learn how to build Python Microservices!
What you'll learn
Learn everything about Python Microservices
Learn how to build the Python Microservices
Learn python development, deployment, scaling, and management
Learn Python and best practices of Python microservices
Requirements
You need to be interested in learning Python Microservices
Description
Python Microservices are an architectural style that structures an application as a collection of loosely coupled services, each of which represents a specific business capability. In Python, microservices have gained popularity due to the language's simplicity, readability, and vast ecosystem of libraries. Each microservice operates independently and communicates with other services over a network, typically through HTTP or messaging protocols. Python has libraries such as Prometheus for monitoring and OpenTelemetry for tracing, which help developers track the health and performance of their services.Python microservices architecture, each service is developed, deployed, and maintained independently. This isolation allows teams to work on different services without worrying about breaking other parts of the system. The microservices paradigm contrasts with monolithic applications, where all functionality is built into a single, large codebase. One key advantage of microservices is scalability. Services can be scaled individually based on their specific load requirements rather than scaling the entire application, as is often necessary with monoliths. Flask and FastAPI are two popular lightweight web frameworks used to build RESTful APIs for microservices. Python's simplicity, wide library support, and ease of integration with modern cloud-native tools make it an excellent choice for building microservices architectures. Python supports several methods, including synchronous HTTP requests and asynchronous message queues like RabbitMQ and Kafka.
Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 Data Visualization and Chats in Python Microservices
Lecture 3 Methods in programming language
Lecture 4 Matplotlib in Python Microservices
Lecture 5 Pandas Dataframe in Python Microservices
Lecture 6 Python lists to Nampy in Python Microservices
Lecture 7 Operating on Nampy arrays in Python Microservices
Lecture 8 Retrieving data frim a data frame
This course is for those wanting to learn Python Microservices
Screenshots
Say "Thank You"
rapidgator.net:
ddownload.com:
