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Data Engineering with Advanced Python: Learn to Build Production Data applications using Modern Cloud Data tools (Data Engineering with Python cookbook series)

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Description

Book Description: Welcome to the "Data Engineering with Advanced Python" book. This comprehensive guide is designed to equip you with the essential skills and knowledge needed to excel in data engineering using the Python programming language. Goal of the Book The goal of this book is to provide you with the tools and techniques necessary to deploy data engineering projects in production environments. It covers coding standards, advanced Python techniques, and their application in popular data engineering tools such as Airflow, Dagster, DBT, Snowflake, and many more Cloud Warehouses. Additionally, the book delves into data engineering design principles, testing strategies, and best practices, ensuring that you can confidently build and maintain robust data applications. Key Features Master Production Standards Learn best practices in Python coding for data engineering, focusing on production-ready code standards. Cover critical topics such as Python dependency management, error handling, and performance optimization. 50+ Hands-On Code Samples Gain practical experience through 50+ coding exercises and 30+ real-world examples, each with step-by-step explanations to reinforce learning. Practical Applications in the Modern Data Stack Work with databases, web APIs, and cloud services. Master data manipulation, web scraping, API interactions, and scalable data processing techniques. Ensuring Code and Data Quality Learn unit testing, code quality tools, and data validation techniques to maintain high-quality data pipelines. Advanced Tooling for Data Engineers Explore DBT, Docker, CI/CD practices, and automation techniques to streamline workflows. Who This Book Is For This book is designed for: Data engineers and software engineers who have a solid understanding of Python. Developers with experience in other programming languages looking to transition into data engineering. Readers of Volume 1, Data Engineering with Python Cookbook, who want to deepen their expertise. How This Book Is Organized Chapters 1-3: Coding Standards & Advanced Data Structures Learn Python coding best practices, software development lifecycle concepts, and data structures for large-scale applications. Coding Exercises: Hands-on exercises with solutions. Chapters 4-6: Advanced Python Concepts Covers inner functions, decorators, generators, and asynchronous programming for efficient data processing. Coding Exercises: Hands-on exercises with solutions. Chapters 7-9: Context Managers, Metaclasses & Secrets Management Learn context managers, metaclasses, handling runtime arguments, and managing sensitive data in engineering workflows. Coding Exercises: Hands-on exercises with solutions. Chapters 10-12: Testing, Automation & Security Covers unit testing, automation frameworks, security vulnerabilities, and logging/monitoring techniques for production systems. Chapters 13-15: Data Engineering Design Patterns & Performance Tuning Discusses scalable design patterns, performance tuning strategies, and common production challenges. Provides insights into real-world issues and best practices for building resilient data applications. A deep dive into tools and frameworks such as Docker, Kubernetes, HELM, DataOps, DataMesh, Data Governance, DBT, Airflow, SQLMesh, and Apache Kafka

Product Specifications

Format: paperback
ASIN: B0DZTDPYWX
Category: Books > Subjects > Computing & Internet > Databases > Applications
Domain: Amazon UK
Release Date: 07 March 2025
Listed Since: 08 March 2025

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Data Engineering with Advanced Python: Learn to Build Production Data applications using Modern Cloud Data tools (Data Engineering with Python cookbook series)

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