Python For Data Pipelines
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Data Engineering With Python
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Author : Paul Crickard
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-10-23
Data Engineering With Python written by Paul Crickard and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-10-23 with Computers categories.
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.
Python For Data Pipelines
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Author : Wolf Blitzer
language : en
Publisher: Independently Published
Release Date : 2025-10-10
Python For Data Pipelines written by Wolf Blitzer and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-10 with Computers categories.
Are your data pipelines slowing you down? Do you want to master Airflow, Dask, and cloud-native ETL like a pro? What if you could build scalable, production-ready data systems that power real-time insights and never break under pressure? In today's data-driven world, the ability to design scalable, automated, and efficient data pipelines separates great engineers from the rest. Python for Data Pipelines: Crafting Scalable ETL Solutions is your complete, hands-on guide to building modern data workflows that can handle anything-from massive batch jobs to real-time analytics across AWS, Google Cloud, and Azure. Whether you're a data engineer, developer, or cloud architect, this book shows you exactly how to move from theory to production using proven frameworks like Apache Airflow and Dask, with deep dives into ETL, ELT, data lakes, and distributed computing. What You'll Learn ✅ Master Apache Airflow - Automate, schedule, and orchestrate complex data workflows with confidence. ✅ Scale with Dask - Process massive datasets in parallel without breaking a sweat. ✅ Go Cloud-Native - Build powerful ETL systems on AWS, GCP, and Azure using Glue, BigQuery, and Data Factory. ✅ Optimize and Monitor - Discover strategies for cost control, fault tolerance, and real-time performance monitoring. ✅ Learn by Doing - Every concept comes with hands-on projects, real-world case studies, and production-ready code. Who This Book Is For Data Engineers who want to build scalable, maintainable pipelines. Python Developers aiming to break into data engineering. Data Scientists seeking to understand how their data is sourced, transformed, and delivered. Cloud Professionals building cost-efficient, automated ETL solutions. Why This Book Stands Out Unlike abstract tutorials, this guide gives you real-world, enterprise-grade examples. You'll see how leading companies in e-commerce, healthcare, and finance solve real data challenges with Python-based pipelines-complete with reusable templates and best practices for production environments. Take Control of Your Data Future If you're ready to design pipelines that scale effortlessly, automate workflows intelligently, and bring true reliability to your data infrastructure - this is the book you've been waiting for.
Python For Data Engineering
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Author : Greyson Chesterfield
language : en
Publisher: Independently Published
Release Date : 2025-01-02
Python For Data Engineering written by Greyson Chesterfield and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-02 with Computers categories.
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
Python For Data Engineering
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Author : NICHOLAS. HOPKINS
language : en
Publisher: Independently Published
Release Date : 2025-07-23
Python For Data Engineering written by NICHOLAS. HOPKINS and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-23 with Computers categories.
Python for Data Engineering: Build Scalable Pipelines, ETL Systems, and Automate Data Workflows Python for Data Engineering is a hands-on, practical guide for building reliable and scalable data systems using Python. Whether you're wrangling datasets, designing ETL pipelines, or automating workflows, this book walks you through every stage of the data engineering lifecycle. From data ingestion and transformation to workflow orchestration and cloud deployment, it equips you with the tools and best practices needed to build production-grade data infrastructure. Designed for both aspiring and experienced data engineers, this book focuses on real-world implementation, covering modern tools such as Apache Airflow, Pandas, Docker, and cloud platforms like AWS and GCP. You'll learn how to process large volumes of data, schedule complex workflows, manage dependencies, and deliver high-quality data pipelines that scale. Master the core skills of modern data engineering using Python. This book starts with fundamental concepts such as working with files, APIs, and databases and gradually moves toward advanced topics like parallel processing, CI/CD for data pipelines, and deploying to the cloud. Each chapter combines theory with step-by-step projects that demonstrate how to solve real engineering problems. Along the way, you'll learn how to debug workflows, document your pipelines, ensure reproducibility, and collaborate effectively in teams. Key Features of This Book Build end-to-end ETL and ELT pipelines using Python and SQL Automate data workflows using Apache Airflow and scheduling tools Connect to APIs, work with cloud storage, and handle large datasets efficiently Implement CI/CD workflows with GitHub Actions for pipeline automation Deploy data solutions on AWS and Google Cloud Follow best practices for version control, testing, documentation, and reproducibility Includes templates, reusable code snippets, and sample configurations This book is ideal for software engineers transitioning into data roles, data analysts looking to level up their engineering skills, and computer science students who want to specialize in backend data systems. It's also a great resource for mid-level data engineers seeking to modernize their workflow with Python-first approaches. Ready to master the tools and techniques of modern data engineering? Python for Data Engineering gives you everything you need to build powerful, automated pipelines that scale. Start building smarter workflows today-your future data infrastructure awaits.
Python Data Engineering Essentials
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Author : Jason Brener
language : en
Publisher: Independently Published
Release Date : 2025-07-18
Python Data Engineering Essentials written by Jason Brener and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Computers categories.
Python Data Engineering Essentials: Learn Pipelines, ETL, and Automation Master the art of building robust, scalable, and automated data pipelines with Python Data Engineering Essentials. This practical guide walks you through the end-to-end lifecycle of modern data workflows from raw data ingestion to clean, production-ready datasets using Python and industry-standard tools. Whether you're transitioning into data engineering or seeking to strengthen your automation skills, this book gives you the confidence and knowledge to tackle real-world challenges. With a strong focus on ETL (Extract, Transform, Load) processes, orchestration, cloud integration, and performance optimization, you'll learn how to design data systems that are not only reliable but also scalable and maintainable. Packed with hands-on code examples, real-life use cases, and deployment strategies, this book helps you move beyond theory and into production. Python Data Engineering Essentials is your one-stop guide to building modern data pipelines with Python. You'll start with the foundations data ingestion, transformation, and storage then dive into tools like Airflow, Docker, SQL, and cloud platforms. You'll learn how to automate workflows, integrate APIs, optimize performance, and handle data at scale with confidence. Each chapter is designed to build on the last, culminating in a real-world project that demonstrates everything you've learned in action. Key Features of This Book Step-by-step tutorials on building ETL and ELT pipelines using Python Practical coverage of orchestration tools like Apache Airflow and Prefect Hands-on integration with cloud services: AWS S3, Google BigQuery, Azure Blob Real-world examples of Docker, version control, CI/CD, and serverless deployment Strategies for performance tuning, error handling, and pipeline observability Interview tips, project ideas, and career guidance for aspiring data engineers This book is ideal for aspiring data engineers, backend developers, data analysts, and software engineers who want to transition into data engineering roles. It's also a solid reference for anyone working with data infrastructure, automation, or analytics platforms using Python. Ready to future-proof your career and build production-grade data pipelines? Python Data Engineering Essentials gives you the tools, workflows, and confidence to thrive in today's data-driven world. Start your journey into professional data engineering one line of Python at a time.
Data Engineering With Python
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Author : Thompson Carter
language : en
Publisher: Independently Published
Release Date : 2024-11-08
Data Engineering With Python written by Thompson Carter and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-08 with Computers categories.
"Data Engineering with Python: Build Efficient Data Pipelines with Python, SQL, and Airflow" Unlock the potential of your data with "Data Engineering with Python," the definitive guide for anyone looking to excel in data-driven decision-making. Whether you're an aspiring data engineer or a professional seeking to build robust data pipelines, this book offers you an in-depth journey through the essential aspects of data engineering. From data collection, storage, and transformation to building and optimizing pipelines using Python, SQL, and Apache Airflow, this book provides hands-on, real-world examples and best practices for success. Learn to manage big data, integrate machine learning into data workflows, and harness cloud platforms like AWS to scale your infrastructure. By the end, you'll have the tools to transform raw data into valuable insights, setting you up for a thriving career in one of today's fastest-growing fields.
Data Engineering With Python
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Author : Thompson Carter
language : en
Publisher: Independently Published
Release Date : 2024-12-15
Data Engineering With Python written by Thompson Carter and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-15 with Computers categories.
Transform your organization's data infrastructure with this comprehensive guide to modern data engineering. Written by industry veterans with decades of experience at Fortune 500 companies, this practical handbook bridges the gap between theoretical concepts and real-world implementation.Discover how to build robust, scalable data pipelines using Python - from ingesting raw data to delivering actionable insights. Through hands-on examples and proven architectural patterns, you'll learn to leverage cutting-edge tools like Apache Airflow, Spark, and cloud services to create production-grade data systems.What Sets This Book Apart: Complete coverage of modern data engineering, from fundamentals to advanced topics Real-world case studies from Netflix, Uber, and other tech giants Step-by-step tutorials for building enterprise-grade data pipelines Cloud-native architectures using AWS, Google Cloud, and Azure Best practices for scalability, monitoring, and security Latest trends in real-time processing, machine learning pipelines, and DataOps Perfect for: Data engineers looking to level up their skills Software engineers transitioning to data roles Data scientists who want to understand pipeline architecture Engineering managers building data teams Students and professionals seeking practical data engineering skills By the end of this book, you'll be able to: Design and implement production-ready data pipelines Build scalable ETL workflows using modern tools Deploy and monitor cloud-based data solutions Optimize performance of big data systems Implement data governance and security best practices Don't miss this opportunity to master the skills that top companies are desperately seeking. Whether you're just starting your data engineering journey or looking to stay ahead of the curve, this book is your definitive guide to building world-class data infrastructure.
Data Engineering With Python Sql
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Author : DIEGO. RODRIGUES
language : en
Publisher: Independently Published
Release Date : 2025-02-09
Data Engineering With Python Sql written by DIEGO. RODRIGUES and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-09 with Computers categories.
Welcome to "DATA ENGINEERING WITH PYTHON AND SQL: Build Scalable Data Pipelines - 2025 Edition," a comprehensive and essential guide for professionals and students who wish to master the art of data engineering in a data-driven world. This book, written by Diego Rodrigues, a best-selling author with over 180 titles published in six languages, combines theory and practice to empower you in building efficient and scalable pipelines. Python and SQL are indispensable tools for data engineers, enabling precise manipulation, integration, and optimization of data workflows. Throughout this book, you will be guided through fundamental and advanced topics, exploring everything from the basics of data engineering to sophisticated strategies for security, governance, and automation of pipelines in both on-premises and cloud environments. Each chapter has been carefully designed to provide practical and applied understanding. You will learn to design database schemas, implement robust ETLs, automate workflows with frameworks such as Apache Airflow, and optimize SQL queries for high performance. Moreover, the book covers emerging topics like DataOps, API integration, and the use of Big Data tools such as Hadoop and Spark. With practical examples, detailed scripts, and clear explanations, "DATA ENGINEERING WITH PYTHON AND SQL" is more than just a technical manual; it is a gateway to a transformative career in the data field. Get ready to stand out in a competitive market and propel your professional journey. Your transformation in data engineering begins now!
Data Pipelines With Apache Airflow
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Author : Bas P. Harenslak
language : en
Publisher: Simon and Schuster
Release Date : 2021-04-27
Data Pipelines With Apache Airflow written by Bas P. Harenslak and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with Computers categories.
For DevOps, data engineers, machine learning engineers, and sysadmins with intermediate Python skills"--Back cover.
Python Data Engineering In Action
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Author : Howley Cahill
language : en
Publisher: Independently Published
Release Date : 2025-11-20
Python Data Engineering In Action written by Howley Cahill and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-20 with Computers categories.
Python Data Engineering in Action is a complete, practical, and modern guide to building production-ready data systems using Python. Whether you're a beginner stepping into data engineering for the first time or a working developer looking to strengthen your pipeline skills, this book gives you everything you need to extract, process, transform, validate, and deploy data reliably at scale.You'll learn how to design end-to-end ETL and ELT pipelines, automate workflows, manage structured and unstructured data, work with streaming sources, optimize performance, and deliver systems that run smoothly in real production environments. Each chapter moves from concept to application, offering detailed explanations, real-world examples, and hands-on Python code you can use immediately.This book does not just teach techniques - it teaches you how to think like a production data engineer. You'll understand how to make trade-offs between batch and streaming systems, structure your transformations, enforce data quality, handle schema changes safely, design robust monitoring, and deploy pipelines confidently using containers and cloud orchestration tools.You will explore essential topics such as: Working with large datasets efficiently using Python, Pandas, Polars, Dask, Ray, and PySparkExtracting data from APIs, files, logs, databases, cloud buckets, and streaming sourcesCleaning, validating, standardizing, and transforming data for analytics and productionWriting scalable pipelines with reusable components and automated testsPerforming incremental loading, partitioning, compaction, and idempotent writesOperating modern data architectures including data lakes, lakehouses, warehouses, and distributed processing systemsDeploying pipelines with Docker, CI/CD, Kubernetes, ECS, and serverless platformsBuilding real-time pipelines with Kafka and message brokersImplementing observability with structured logging, metrics, alerts, and troubleshooting workflowsDesigning hybrid batch/streaming architectures and maintaining them long-termEvery concept is explained clearly so you can use it immediately, and each chapter includes insights drawn from real production systems. By the end of this book, you'll know how to build data platforms that are dependable, well-structured, easy to extend, and ready for the scale and complexity of modern data workloads.Who This Book Is ForAspiring data engineersSoftware developers expanding into data engineeringPython engineers interested in ETL, streaming, or distributed systemsAnalysts transitioning to pipeline developmentStudents and professionals preparing for data engineering rolesTeams who want to design consistent, reliable data systemsNo prior experience with distributed computing or cloud platforms is required. The book guides you carefully from simple foundations to advanced, production-grade patterns.Why This Book Stands OutUnlike many resources that only cover theory or isolated examples, this book gives you a complete and practical path from extraction to deployment. You will gain: Real production patternsAccurate and authentic coding examplesReusable templates and checklistsTroubleshooting guidanceDeployment-ready workflowsClear explanations without unnecessary jargonIf you want to build data pipelines that work reliably - not just in controlled examples but in actual production environments - this book is your blueprint.Call to ActionReady to build real data systems that solve real problems? Take the next step in your career and transform the way you handle data.