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Asynchronous Programming in Python

Asynchronous Programming in Python

By : Nicolas Bohorquez
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Asynchronous Programming in Python

Asynchronous Programming in Python

By: Nicolas Bohorquez

Overview of this book

Asynchronous programming is one of the most effective but often misunderstood techniques for building fast, scalable, and responsive systems in Python. While it can significantly improve performance, efficiency, and sustainability, using async without a clear understanding of its trade-offs can lead to fragile designs and hard-to-debug issues. This book offers a structured approach to applying asynchronous programming in Python. It begins with a conceptual framework to help you distinguish between synchronous and asynchronous execution models, and shows how async relates to other concurrency strategies such as multithreading and multiprocessing. From there, you will explore the core tools available for building async applications in Python. You will also learn how to measure the impact of async programming in practical scenarios, profile and debug asynchronous code, and evaluate performance improvements using real-world metrics. The final chapters focus on applying async techniques to common cloud-based systems, such as web frameworks, database interactions, and data-pipelines tools. Designed for developers looking to apply async programming with confidence, this book blends real-world examples with core concepts to help you write efficient, maintainable Python code.
Table of Contents (14 chapters)
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12
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Index

Asynchronous Data Pipelines

From the early 2010s onwards, multiple companies rushed into developing frameworks and tools to handle the ‘Big Data’ they generated, but many discovered that the amount of data processed was in fact rather small. The problem was that traditional techniques were often found wanting when it came to processing the data available in new formats and structures.

There were business lessons there, but the response from the tech industry was to flex the boundaries of traditional processing models and offer lightweight, local-first solutions for processing data. A well-written sequence of independent but related data processing tasks is the common basis from which technology and product teams start when designing processes today.

A common use case is the processing of datasets, where a dataset is a collection of related data. In a perfect world all data would be well structured and consistent, but in real-life situations you typically need...

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