Python For Data Engineering
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Data Engineering with Python
Author: Paul Crickard
language: en
Publisher: Packt Publishing Ltd
Release Date: 2020-10-23
Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.
Mastering Python for Data Engineering
Author: Thompson Carter
language: en
Publisher: Independently Published
Release Date: 2025-01-09
Mastering Python for Data Engineering: Transform and Manipulate Big Data with Python Unlock the true potential of Python for big data manipulation and engineering with Mastering Python for Data Engineering. This comprehensive guide is designed to help data engineers and aspiring professionals transform, process, and analyze massive datasets efficiently. By leveraging Python's powerful libraries and tools, you'll be equipped to build scalable data pipelines, integrate various data sources, and optimize data workflows for performance. From basic data wrangling to advanced engineering techniques, this book provides a practical, hands-on approach to mastering data engineering tasks with Python, making it the perfect companion for anyone aiming to work with big data. What You'll Learn: The fundamentals of Python for data engineering, including essential libraries like pandas, NumPy, and Dask. Building efficient data pipelines for ETL (Extract, Transform, Load) processes. Working with large datasets using parallel and distributed processing tools like Apache Spark and Dask. Integrating data from various sources, such as databases, APIs, and streaming data. Data transformation and cleaning techniques to prepare data for analysis. Optimizing performance and scaling data workflows with Python. With step-by-step guidance and practical examples, Mastering Python for Data Engineering will show you how to handle data at scale, integrate different data sources, and build automated data workflows that are crucial for modern data infrastructure. Dive into the world of data engineering with Python and learn how to transform raw data into actionable insights while building systems that can handle vast amounts of information.
Python for Data Engineering
Author: Greyson Chesterfield
language: en
Publisher: Independently Published
Release Date: 2025-01-02
Python for Data Engineering: Build ETL Pipelines and Handle Big Data Efficiently with Python Unlock the full potential of data engineering with "Python for Data Engineering", the essential guide for aspiring data engineers, data scientists, and IT professionals seeking to master the art of building robust ETL pipelines and managing big data using Python. Whether you're just beginning your data engineering journey or looking to enhance your existing skills, this comprehensive handbook provides the tools, techniques, and insights necessary to transform raw data into valuable assets for your organization. Dive into expertly structured chapters that blend theoretical knowledge with practical applications, covering everything from the fundamentals of data engineering and Python programming to advanced topics like distributed computing, real-time data processing, and cloud integration. Learn how to design, develop, and deploy scalable ETL pipelines that efficiently extract, transform, and load data from diverse sources. Discover best practices for handling large datasets, optimizing performance, and ensuring data quality and integrity throughout the data lifecycle. "Python for Data Engineering" empowers you to: Master ETL Processes: Understand the core principles of ETL and learn how to implement efficient data extraction, transformation, and loading strategies using Python. Handle Big Data: Explore techniques for managing and processing large-scale datasets with tools like Apache Spark, Hadoop, and Dask, all within the Python ecosystem. Automate Workflows: Streamline data engineering tasks by automating repetitive processes with Python scripts and workflow management tools such as Airflow and Luigi. Design Scalable Pipelines: Build resilient and scalable data pipelines that can handle increasing data volumes and complexity with ease. Ensure Data Quality: Implement robust data validation, cleansing, and monitoring practices to maintain high-quality data standards. Leverage Cloud Services: Integrate Python-based data engineering solutions with leading cloud platforms like AWS, Google Cloud, and Azure for enhanced flexibility and scalability. Optimize Performance: Fine-tune your data engineering workflows for maximum efficiency, reducing latency and improving throughput. Implement Security Best Practices: Protect sensitive data by applying security measures and ensuring compliance with industry standards and regulations. Visualize and Report Data: Create insightful visualizations and reports to communicate data findings effectively using libraries like Matplotlib, Seaborn, and Plotly. Stay Ahead with Advanced Topics: Delve into cutting-edge technologies such as machine learning integration, real-time analytics, and serverless computing to keep your skills current and in demand. Packed with real-world examples, hands-on exercises, and expert tips, "Python for Data Engineering" serves as your indispensable companion in navigating the dynamic field of data engineering. Whether you're building data pipelines for business intelligence, supporting data-driven decision-making, or driving innovation through data analytics, this book equips you with the knowledge and skills to excel. Key Features: Comprehensive coverage of data engineering fundamentals and advanced Python techniques Step-by-step tutorials for building and deploying ETL pipelines In-depth guides to handling and processing big data with Python-based tools Real-world case studies illustrating best practices and common challenges Practical exercises and projects to reinforce learning and develop hands-on experience Insights into the latest trends and technologies in the data engineering landscape