Data Engineering With Python
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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.
97 Things Every Data Engineer Should Know
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Author : Tobias Macey
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-11
97 Things Every Data Engineer Should Know written by Tobias Macey and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Computers categories.
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail
Mastering Python For Data Engineering
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Author : Thompson Carter
language : en
Publisher: Independently Published
Release Date : 2025-01-09
Mastering Python For Data Engineering 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 2025-01-09 with Computers categories.
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
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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
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!
Master Python Data Engineering With Virtual Ai Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Data Engineering With Virtual Ai Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine acquiring a book and, as a bonus, gaining access to a 24/7 AI-assisted Virtual Tutoring to personalize your learning journey, reinforce knowledge, and receive mentorship for developing and implementing real projects... ...Welcome to the Revolution of Personalized Learning with AI-Assisted Virtual Tutoring! Discover " MASTER PYTHON DATA ENGINEERING: From Fundamentals to Advanced Applications with Virtual AI Tutoring," the essential guide for professionals and enthusiasts who want to master data engineering with Python. This innovative manual, written by Diego Rodrigues, an author with over 140 titles published in six languages, combines high-quality content with the advanced technology of IAGO, a virtual tutor developed and hosted on the OpenAI platform. Innovative Features: Personalized Learning: IAGO adapts the content to your knowledge level, offering detailed explanations and personalized exercises. Immediate Feedback: Receive corrections and suggestions in real time, speeding up your learning process. Interactivity and Engagement: Interact with the tutor via text or voice, making learning more dynamic and motivating. Project Development Mentorship: Get practical guidance to develop and implement real projects, applying the knowledge gained. Total Flexibility: Access the tutor anywhere, anytime, whether on a desktop, notebook, or smartphone with web access. Take advantage of the Limited-Time Launch Promotional Price! Don't miss the opportunity to transform your learning journey with an innovative and effective method. This book has been carefully structured to meet your needs and exceed your expectations, ensuring you are prepared to face challenges and seize opportunities in the field of data engineering. Open the book sample and discover how to access the select club of cutting-edge technology professionals. Take advantage of this unique opportunity and achieve your goals! TAGS: data engineering automation science big Pandas NumPy Dask SQLAlchemy web scraping BeautifulSoup Scrapy APIs ETL DataOps Data Lakes Data Warehouses AWS Google Cloud Microsoft Azure Hadoop Spark machine learning artificial intelligence data pipelines data visualization Matplotlib Seaborn data analysis relational databases NoSQL MongoDB Apache Airflow Kafka real-time data governance data security compliance mentorship Diego Rodrigues Tableau Power BI Snowflake Informatica Alation Talend Apache Flink Jupyter Notebooks DevOps Databricks Cloudera Hortonworks Teradata IBM Cloud Oracle Cloud Salesforce SAP HANA ElasticSearch Redis Kubernetes Docker Jenkins GitHub GitLab Continuous Integration Continuous Deployment CI/CD digital transformation predictive analysis business intelligence IoT Internet of Things smart cities connected health Industry 4.0 fintechs retail education marketing competitive intelligence data science automated testing custom reports operational efficiency Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
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.
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 : MIGUEL. FARMER
language : en
Publisher: Independently Published
Release Date : 2025-07-22
Data Engineering With Python written by MIGUEL. FARMER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-22 with Computers categories.
Turn Raw Data into Reliable Insights-One Pipeline at a Time. In a world where data is the new oil, being able to move, clean, transform, and store data efficiently is a game-changing skill. Data Engineering with Python gives you the tools and know-how to build end-to-end ETL pipelines and robust data workflows-from scratch. This book is built for developers, analysts, and aspiring data engineers who want a clear, hands-on guide to real-world data engineering. You'll master how to extract data from APIs and databases, clean and structure it using Python, and load it into data warehouses for downstream analysis. With step-by-step walkthroughs, best practices, and scalable architecture patterns, you'll go beyond the theory and start building production-grade systems. Whether you're working with batch or streaming data, local files or cloud services-this book will equip you with the Python-first approach to build pipelines that are scalable, maintainable, and ready for the modern data stack. ✅ What You'll Learn: ETL fundamentals and how to build pipelines from zero Using Pandas, SQLAlchemy, and Airflow for real-world workflows Connecting to APIs, CSVs, SQL, NoSQL, and cloud storage Data validation, logging, and error handling for clean pipelines Introduction to orchestration, scheduling, and automation Best practices for modular, testable pipeline code Perfect For: Aspiring data engineers and developers Data analysts looking to automate and scale workflows Backend engineers working with data-heavy applications Anyone transitioning into data engineering roles