Model And Data Engineering
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Model And Data Engineering
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Author : Yamine Ait Ameur
language : en
Publisher: Springer
Release Date : 2014-09-19
Model And Data Engineering written by Yamine Ait Ameur and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-19 with Computers categories.
This book constitutes the refereed proceedings of the 4th International Conference on Model and Data Engineering, MEDI 2014, held in Larnaca, Cyprus, in September 2014. The 16 long papers and 12 short papers presented together with 2 invited talks were carefully reviewed and selected from 64 submissions. The papers specifically focus on model engineering and data engineering with special emphasis on most recent and relevant topics in the areas of modeling and models engineering; data engineering; modeling for data management; and applications and tooling.
Model And Data Engineering
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Author : Yassine Ouhammou
language : en
Publisher: Springer
Release Date : 2017-09-18
Model And Data Engineering written by Yassine Ouhammou and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-18 with Computers categories.
This book constitutes the refereed proceedings of the 7th International Conference on Model and Data Engineering, MEDI 2017, held in Barcelona, Spain, in October 2017. The 20 full papers and 7 short papers presented together with 2 invited talks were carefully reviewed and selected from 69 submissions. The papers are organized in topical sections on domain specific languages; systems and software assessments; modeling and formal methods; data engineering; data exploration and exp loitation; modeling heterogeneity and behavior; model-based applications; and ontology-based applications.
A Practical Guide To Data Engineering
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Author : Pedram Ariel Rostami
language : en
Publisher: Starseed AI
Release Date :
A Practical Guide To Data Engineering written by Pedram Ariel Rostami and has been published by Starseed AI this book supported file pdf, txt, epub, kindle and other format this book has been release on with Education categories.
"A Practical Guide to Machine Learning and AI: Part-I" is an essential resource for anyone looking to dive into the world of artificial intelligence and machine learning. Whether you're a complete beginner or have some experience in the field, this book will equip you with the fundamental knowledge and hands-on skills needed to harness the power of these transformative technologies. In this comprehensive guide, you'll embark on an engaging journey that starts with the basics of data engineering. You'll gain a solid understanding of big data, the key roles involved, and how to leverage the versatile Python programming language for data-centric tasks. From mastering Python data types and control structures to exploring powerful libraries like NumPy and Pandas, you'll build a strong foundation to tackle more advanced concepts. As you progress, the book delves into the realm of exploratory data analysis (EDA), where you'll learn techniques to clean, transform, and extract insights from your data. This sets the stage for the heart of the book - machine learning. You'll explore both supervised and unsupervised learning, diving deep into regression, classification, clustering, and dimensionality reduction algorithms. Along the way, you'll encounter real-world examples and hands-on exercises to reinforce your understanding and apply what you've learned. But this book goes beyond just the technical aspects. It also addresses the ethical considerations surrounding machine learning, ensuring you develop a well-rounded perspective on the responsible use of these powerful tools. Whether your goal is to jumpstart a career in data science, enhance your existing skills, or simply satisfy your curiosity about the latest advancements in AI, "A Practical Guide to Machine Learning and AI: Part-I" is your comprehensive companion. Prepare to embark on an enriching journey that will equip you with the knowledge and skills to navigate the exciting frontiers of artificial intelligence and machine learning.
Data Engineering Best Practices
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Author : Richard J. Schiller
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-10-11
Data Engineering Best Practices written by Richard J. Schiller 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 2024-10-11 with Computers categories.
Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
Data Observability For Data Engineering
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Author : Michele Pinto
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-12-29
Data Observability For Data Engineering written by Michele Pinto 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 2023-12-29 with Computers categories.
Discover actionable steps to maintain healthy data pipelines to promote data observability within your teams with this essential guide to elevating data engineering practices Key Features Learn how to monitor your data pipelines in a scalable way Apply real-life use cases and projects to gain hands-on experience in implementing data observability Instil trust in your pipelines among data producers and consumers alike Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the age of information, strategic management of data is critical to organizational success. The constant challenge lies in maintaining data accuracy and preventing data pipelines from breaking. Data Observability for Data Engineering is your definitive guide to implementing data observability successfully in your organization. This book unveils the power of data observability, a fusion of techniques and methods that allow you to monitor and validate the health of your data. You’ll see how it builds on data quality monitoring and understand its significance from the data engineering perspective. Once you're familiar with the techniques and elements of data observability, you'll get hands-on with a practical Python project to reinforce what you've learned. Toward the end of the book, you’ll apply your expertise to explore diverse use cases and experiment with projects to seamlessly implement data observability in your organization. Equipped with the mastery of data observability intricacies, you’ll be able to make your organization future-ready and resilient and never worry about the quality of your data pipelines again.What you will learn Implement a data observability approach to enhance the quality of data pipelines Collect and analyze key metrics through coding examples Apply monkey patching in a Python module Manage the costs and risks associated with your data pipeline Understand the main techniques for collecting observability metrics Implement monitoring techniques for analytics pipelines in production Build and maintain a statistics engine continuously Who this book is for This book is for data engineers, data architects, data analysts, and data scientists who have encountered issues with broken data pipelines or dashboards. Organizations seeking to adopt data observability practices and managers responsible for data quality and processes will find this book especially useful to increase the confidence of data consumers and raise awareness among producers regarding their data pipelines.
Data Engineering Fundamentals
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Author : Zhaolong Liu
language : en
Publisher: BPB Publications
Release Date : 2025-03-30
Data Engineering Fundamentals written by Zhaolong Liu and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-30 with Computers categories.
DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for anyone looking to build robust data pipelines and extract valuable insights. This book simplifies complex concepts and provides a clear pathway to understanding the core principles that power modern data solutions. It bridges the gap between raw data and actionable intelligence, making data engineering accessible to everyone. This book walks you through the entire data engineering lifecycle. Starting with foundational concepts and data ingestion from diverse sources, you will learn how to build efficient data lakes and warehouses. You will learn data transformation using tools like Apache Spark and the orchestration of data workflows with platforms like Airflow and Argo Workflow. Crucial aspects of data quality, governance, scalability, and performance monitoring are thoroughly covered, ensuring you understand how to maintain reliable and efficient data systems. Real-world use cases across industries like e-commerce, finance, and government illustrate practical applications, while a final section explores emerging trends such as AI integration and cloud advancements. By the end of this book, you will have a solid foundation in data engineering, along with practical skills to help enhance your career. You will be equipped to design, build, and maintain data pipelines, transforming raw data into meaningful insights. WHAT YOU WILL LEARN ● Understand data engineering base concepts and build scalable solutions. ● Master data storage, ingestion, and transformation. ● Orchestrates data workflows and automates pipelines for efficiency. ● Ensure data quality, governance, and security compliance. ● Monitor, optimize, and scale data solutions effectively. ● Explore real-world use cases and future data trends. WHO THIS BOOK IS FOR This book is for aspiring data engineers, analysts, and developers seeking a foundational understanding of data engineering. Whether you are a beginner or looking to deepen your expertise, this book provides you with the knowledge and tools to succeed in today’s data engineering challenges. TABLE OF CONTENTS 1. Understanding Data Engineering 2. Data Ingestion and Acquisition 3. Data Storage and Management 4. Data Transformation and Processing 5. Data Orchestration and Workflows 6. Data Governance Principles 7. Scaling Data Solutions 8. Monitoring and Performance 9. Real-world Data Engineering Use Cases 10. Future Trends in Data Engineering
Complete Data Engineering In 8 Hours
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Author : QuickTechie | A career growth machine
language : en
Publisher: PappuPass Learning Resources
Release Date : 2025-02-02
Complete Data Engineering In 8 Hours written by QuickTechie | A career growth machine and has been published by PappuPass Learning Resources this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-02 with Computers categories.
"Complete Data Engineering in 8 Hours" is a fast-paced learning guide designed to equip both beginners and experienced professionals with the essential skills required to excel in the field of data engineering. In today's digital age, data is paramount, driving decision-making, automation, and innovation. As QuickTechie.com emphasizes, the role of a Data Engineer is increasingly vital for organizations needing to manage, process, and analyze large volumes of data effectively. This book addresses the growing need for skilled professionals who can navigate the complexities of modern data infrastructure. This book offers a structured approach, providing practical insights into core data engineering concepts. It covers essential areas such as databases, data pipelines, Extract, Transform, Load (ETL) processes, big data technologies, and cloud platforms. Unlike traditional lengthy textbooks, this guide is designed to provide a quick yet comprehensive understanding within a targeted timeframe, allowing readers to quickly grasp fundamental principles and advanced techniques. Readers can expect to follow a step-by-step learning path, mastering the art of designing, building, and scaling data systems efficiently. The book ensures readers gain practical, industry-relevant skills that can be immediately applied in a professional setting. This makes it an excellent resource for those transitioning into the field, those aiming to upskill in their current roles, or individuals preparing for data engineering job interviews. By the end of "Complete Data Engineering in 8 Hours," readers will possess the knowledge and confidence to develop, implement, and optimize data infrastructure. This will empower them to become highly valued assets in the data-driven world, capable of contributing significantly to an organization's data strategies. The book is not just a theoretical guide; it provides hands-on learning opportunities to translate theoretical knowledge into practical skills, aligning with QuickTechie.com commitment to practical, applicable technology learning.
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Author :
language : en
Publisher: "O'Reilly Media, Inc."
Release Date :
written by 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 with categories.
Model And Data Engineering
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Author : Ladjel Bellatreche
language : en
Publisher: Springer
Release Date : 2011-10-07
Model And Data Engineering written by Ladjel Bellatreche and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-10-07 with Computers categories.
This book constitutes the refereed proceedings of the First International Conference on Model and Data Engineering, MEDI 2011, held in Óbidos, Portugal, in September 2011. The 18 revised full papers presented together with 8 short papers and three keynotes were carefully reviewed and selected from 67 submissions. The papers are organized in topical sections on ontology engineering; Web services and security; advanced systems; knowledge management; model specification and verification; and models engineering.
Model And Data Engineering
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Author : El Hassan Abdelwahed
language : en
Publisher: Springer
Release Date : 2018-10-19
Model And Data Engineering written by El Hassan Abdelwahed and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-19 with Computers categories.
This book constitutes the refereed proceedings of the 8h International Conference on Model and Data Engineering, MEDI 2018, held in Marrakesh, Morocco, in October 2018. The 23 full papers and 4 short papers presented together with 2 invited talks were carefully reviewed and selected from 86 submissions. The papers covered the recent and relevant topics in the areas of databases; ontology and model-driven engineering; data fusion, classsification and learning; communication and information technologies; safety and security; algorithms and text processing; and specification, verification and validation.