Data Engineering And Business Intelligence For Scalable Solutions
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Data Engineering And Business Intelligence For Scalable Solutions
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Author : RAVI KIRAN PAGIDI PROF.(DR.) VISHWADEEPAK SINGH BAGHELA
language : en
Publisher: DeepMisti Publication
Release Date : 2024-12-22
Data Engineering And Business Intelligence For Scalable Solutions written by RAVI KIRAN PAGIDI PROF.(DR.) VISHWADEEPAK SINGH BAGHELA and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-22 with Computers categories.
In the dynamic realm of data engineering and business intelligence, scalability is no longer a luxury but a necessity for organizations aiming to thrive in today’s data-driven world. This book, Data Engineering and Business Intelligence for Scalable Systems, is crafted to address the challenges and opportunities involved in designing, implementing, and managing scalable solutions that transform raw data into actionable insights. Our mission is to provide a comprehensive resource that bridges the gap between foundational principles and cutting-edge strategies, equipping readers with the knowledge to excel in this fast-evolving field. This book delves deeply into the methodologies, tools, and frameworks that underpin successful data engineering and business intelligence practices for scalable systems. From conceptualizing robust data pipelines to leveraging advanced analytics for decision-making, the content spans a wide range of topics tailored to meet the needs of students, data engineers, BI professionals, and organizational leaders. Through a balanced approach, we integrate theory with practical applications, offering readers actionable insights to tackle real-world challenges in data scalability and intelligence. The chapters are meticulously structured to provide both depth and breadth, covering topics such as data architecture design, ETL processes, cloud-based data warehousing, and real-time analytics. Furthermore, we explore the integration of machine learning into BI systems, the use of automation in data workflows, and the role of predictive modeling in crafting forward-looking business strategies. Special emphasis is placed on scalability, ensuring that the solutions discussed are adaptable to growing data volumes and evolving enterprise demands. We hope this book serves as a trusted guide for those aspiring to master the art and science of data engineering and business intelligence for scalable systems. May it inspire innovation, foster growth, and empower readers to design systems that stand at the forefront of technological and business advancements. Thank you for joining us on this transformative journey. Authors
Data Engineering With Google Cloud Platform
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Author : Adi Wijaya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-03-31
Data Engineering With Google Cloud Platform written by Adi Wijaya 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 2022-03-31 with Computers categories.
Build and deploy your own data pipelines on GCP, make key architectural decisions, and gain the confidence to boost your career as a data engineer Key Features Understand data engineering concepts, the role of a data engineer, and the benefits of using GCP for building your solution Learn how to use the various GCP products to ingest, consume, and transform data and orchestrate pipelines Discover tips to prepare for and pass the Professional Data Engineer exam Book DescriptionWith this book, you'll understand how the highly scalable Google Cloud Platform (GCP) enables data engineers to create end-to-end data pipelines right from storing and processing data and workflow orchestration to presenting data through visualization dashboards. Starting with a quick overview of the fundamental concepts of data engineering, you'll learn the various responsibilities of a data engineer and how GCP plays a vital role in fulfilling those responsibilities. As you progress through the chapters, you'll be able to leverage GCP products to build a sample data warehouse using Cloud Storage and BigQuery and a data lake using Dataproc. The book gradually takes you through operations such as data ingestion, data cleansing, transformation, and integrating data with other sources. You'll learn how to design IAM for data governance, deploy ML pipelines with the Vertex AI, leverage pre-built GCP models as a service, and visualize data with Google Data Studio to build compelling reports. Finally, you'll find tips on how to boost your career as a data engineer, take the Professional Data Engineer certification exam, and get ready to become an expert in data engineering with GCP. By the end of this data engineering book, you'll have developed the skills to perform core data engineering tasks and build efficient ETL data pipelines with GCP.What you will learn Load data into BigQuery and materialize its output for downstream consumption Build data pipeline orchestration using Cloud Composer Develop Airflow jobs to orchestrate and automate a data warehouse Build a Hadoop data lake, create ephemeral clusters, and run jobs on the Dataproc cluster Leverage Pub/Sub for messaging and ingestion for event-driven systems Use Dataflow to perform ETL on streaming data Unlock the power of your data with Data Studio Calculate the GCP cost estimation for your end-to-end data solutions Who this book is for This book is for data engineers, data analysts, and anyone looking to design and manage data processing pipelines using GCP. You'll find this book useful if you are preparing to take Google's Professional Data Engineer exam. Beginner-level understanding of data science, the Python programming language, and Linux commands is necessary. A basic understanding of data processing and cloud computing, in general, will help you make the most out of this book.
Data Engineering For Cloud Applications Leveraging Full Stack Skills For Scalable Solutions
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Author : AKASH BALAJI MALI PROF. (DR.) SUDEEPT SINGH YADAV
language : en
Publisher: DeepMisti Publication
Release Date :
Data Engineering For Cloud Applications Leveraging Full Stack Skills For Scalable Solutions written by AKASH BALAJI MALI PROF. (DR.) SUDEEPT SINGH YADAV and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
In the rapidly evolving world of cloud computing, data engineering plays a pivotal role in building scalable, efficient, and resilient applications. As organizations move their infrastructures to the cloud, the demand for professionals who can design, manage, and optimize data pipelines has surged. "Data Engineering for Cloud Applications: Leveraging Full-Stack Skills for Scalable Solutions" aims to bridge the gap between traditional data engineering practices and the modern demands of cloud-native environments. This book is written for developers, engineers, and architects who want to harness the power of cloud platforms while leveraging their full-stack skills to create scalable, high-performance applications. The integration of cloud technologies such as AWS, Azure, and Google Cloud with data engineering practices enables organizations to manage vast amounts of data effectively, streamline their workflows, and enhance decision-making capabilities. Through practical insights, hands-on examples, and industry best practices, this book guides you through the entire data engineering lifecycle in the cloud, from ingestion to processing and storage. Emphasis is placed on optimizing data flows, reducing latency, and maintaining data integrity across distributed systems. Whether you're working with relational databases, NoSQL systems, or big data solutions, this book offers the tools and techniques necessary to build applications that scale with your business needs. Moreover, this book highlights the synergy between cloud architecture and full-stack development, demonstrating how data engineers can collaborate with front-end and back-end developers to create end-to-end solutions. By the end, you will have a deep understanding of cloud data engineering, allowing you to design robust, scalable solutions that meet the demands of modern businesses in an increasingly data-driven world. Thank you for embarking on this journey with us. Authors
Aws Certified Data Engineer Study Guide
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Author : Syed Humair
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-13
Aws Certified Data Engineer Study Guide written by Syed Humair and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
Your complete Guide to preparing for the AWS® Certified Data Engineer: Associate exam The AWS® Certified Data Engineer Study Guide is your one-stop resource for complete coverage of the challenging DEA-C01 Associate exam. This Sybex Study Guide covers 100% of the DEA-C01 objectives. Prepare for the exam faster and smarter with Sybex thanks to accurate content including, an assessment test that validates and measures exam readiness, real-world examples and scenarios, practical exercises, and challenging chapter review questions. Reinforce and retain what you’ve learned with the Sybex online learning environment and test bank, accessible across multiple devices. Get ready for the AWS Certified Data Engineer exam – quickly and efficiently – with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you’ll be ready for: Data Ingestion and Transformation Data Store Management Data Operations and Support Data Security and Governance ABOUT THE AWS DATA ENGINEER – ASSOCIATE CERTIFICATION The AWS Data Engineer – Associate certification validates skills and knowledge in core data-related Amazon Web Services. It recognizes your ability to implement data pipelines and to monitor, troubleshoot, and optimize cost and performance issues in accordance with best practices Interactive learning environment Take your exam prep to the next level with Sybex’s superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/go/sybextestprep, register your book to receive your unique PIN, and instantly gain one year of FREE access after activation to: • Interactive test bank with 5 practice exams to help you identify areas where further review is needed. Get more than 90% of the answers correct, and you’re ready to take the certification exam. • 100 electronic flashcards to reinforce learning and last-minute prep before the exam • Comprehensive glossary in PDF format gives you instant access to the key terms so you are fully prepared
Data Engineering On The Cloud A Practical Guide 2025
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Author : Raghu Gopa, Dr. Arpita Roy
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :
Data Engineering On The Cloud A Practical Guide 2025 written by Raghu Gopa, Dr. Arpita Roy and has been published by YASHITA PRAKASHAN PRIVATE LIMITED this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
PREFACE The digital transformation of businesses and the exponential growth of data have created a fundamental shift in how organizations approach data management, analytics, and decision-making. As cloud technologies continue to evolve, cloud-based data engineering has become central to the success of modern data-driven enterprises. “Data Engineering on the Cloud: A Practical Guide” aims to equip data professionals, engineers, and organizations with the knowledge and practical tools needed to build and manage scalable, secure, and efficient data engineering pipelines in cloud environments. This book is designed to bridge the gap between the theoretical foundations of data engineering and the practical realities of working with cloud-based data platforms. Cloud computing has revolutionized data storage, processing, and analytics by offering unparalleled scalability, flexibility, and cost efficiency. However, with these opportunities come new challenges, including selecting the right tools, architectures, and strategies to ensure seamless data integration, transformation, and delivery. As businesses increasingly migrate their data to the cloud, it is essential for data engineers to understand how to leverage the capabilities of the cloud to build robust data pipelines that can handle large, complex datasets in real-time. Throughout this guide, we will explore the various facets of cloud-based data engineering, from understanding cloud storage and computing services to implementing data integration techniques, managing data quality, and optimizing performance. Whether you are building data pipelines from scratch, migrating on-premises systems to the cloud, or enhancing existing data workflows, this book will provide actionable insights and step-by-step guidance on best practices, tools, and frameworks commonly used in cloud data engineering. Key topics covered in this book include: · The fundamentals of cloud architecture and the role of cloud providers (such as AWS, Google Cloud, and Microsoft Azure) in data engineering workflows. · Designing scalable and efficient data pipelines using cloud-based tools and services. · Integrating diverse data sources, including structured, semi-structured, and unstructured data, for seamless processing and analysis. · Data transformation techniques, including ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform), in cloud environments. · Ensuring data quality, governance, and security when working with cloud data platforms. · Optimizing performance for data storage, processing, and analytics to handle growing data volumes and complexity. This book is aimed at professionals who are already familiar with data engineering concepts and are looking to apply those concepts within cloud environments. It is also suitable for organizations that are in the process of migrating to cloud-based data platforms and wish to understand the nuances and best practices for cloud data engineering. In addition to theoretical knowledge, this guide emphasizes hands-on approaches, providing practical examples, code snippets, and real-world case studies to demonstrate the effective implementation of cloud-based data engineering solutions. We will explore how to utilize cloud-native services to streamline workflows, improve automation, and reduce manual interventions in data pipelines. Throughout the book, you will gain insights into the evolving tools and technologies that make data engineering more agile, reliable, and efficient. The role of data engineering is growing ever more important in enabling businesses to unlock the value of their data. By the end of this book, you will have a comprehensive understanding of how to leverage cloud technologies to build high-performance, scalable data engineering solutions that are aligned with the needs of modern data-driven organizations. We hope this guide helps you to navigate the complexities of cloud data engineering and helps you unlock new possibilities for your data initiatives. Welcome to “Data Engineering on the Cloud: A Practical Guide.” Let’s embark on this journey to harness the full potential of cloud technologies in the world of data engineering. Authors
Data Engineering For Ai Ml Pipelines
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Author : Venkata Karthik Penikalapati
language : en
Publisher: BPB Publications
Release Date : 2024-10-18
Data Engineering For Ai Ml Pipelines written by Venkata Karthik Penikalapati and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-18 with Computers categories.
DESCRIPTION Data engineering is the art of building and managing data pipelines that enable efficient data flow for AI/ML projects. This book serves as a comprehensive guide to data engineering for AI/ML systems, equipping you with the knowledge and skills to create robust and scalable data infrastructure. This book covers everything from foundational concepts to advanced techniques. It begins by introducing the role of data engineering in AI/ML, followed by exploring the lifecycle of data, from data generation and collection to storage and management. Readers will learn how to design robust data pipelines, transform data, and deploy AI/ML models effectively for real-world applications. The book also explains security, privacy, and compliance, ensuring responsible data management. Finally, it explores future trends, including automation, real-time data processing, and advanced architectures, providing a forward-looking perspective on the evolution of data engineering. By the end of this book, you will have a deep understanding of the principles and practices of data engineering for AI/ML. You will be able to design and implement efficient data pipelines, select appropriate technologies, ensure data quality and security, and leverage data for building successful AI/ML models. KEY FEATURES ● Comprehensive guide to building scalable AI/ML data engineering pipelines. ● Practical insights into data collection, storage, processing, and analysis. ● Emphasis on data security, privacy, and emerging trends in AI/ML. WHAT YOU WILL LEARN ● Architect scalable data solutions for AI/ML-driven applications. ● Design and implement efficient data pipelines for machine learning. ● Ensure data security and privacy in AI/ML systems. ● Leverage emerging technologies in data engineering for AI/ML. ● Optimize data transformation processes for enhanced model performance. WHO THIS BOOK IS FOR This book is ideal for software engineers, ML practitioners, IT professionals, and students wanting to master data pipelines for AI/ML. It is also valuable for developers and system architects aiming to expand their knowledge of data-driven technologies. TABLE OF CONTENTS 1. Introduction to Data Engineering for AI/ML 2. Lifecycle of AI/ML Data Engineering 3. Architecting Data Solutions for AI/ML 4. Technology Selection in AI/ML Data Engineering 5. Data Generation and Collection for AI/ML 6. Data Storage and Management in AI/ML 7. Data Ingestion and Preparation for ML 8. Transforming and Processing Data for AI/ML 9. Model Deployment and Data Serving 10. Security and Privacy in AI/ML Data Engineering 11. Emerging Trends and Future Direction
Mastering Big Data Engineering Aws Gcp Azure Showdown
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Author : Muthuraman Saminathan
language : en
Publisher: Libertatem Media Private Limited
Release Date : 2024-02-16
Mastering Big Data Engineering Aws Gcp Azure Showdown written by Muthuraman Saminathan and has been published by Libertatem Media Private Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-16 with Business & Economics categories.
In the rapidly evolving field of AI, operationalizing large language models (LLMs) has become a defining challenge. The LLMOps Advantage: Navigating the Future of AI is your comprehensive guide to mastering the deployment, monitoring, and scaling of LLMs in real-world applications. This book bridges the gap between model development and production, introducing readers to the specialized domain of LLMOps—a subset of MLOps tailored to the unique demands of large language models. From building scalable pipelines and optimizing inference workflows to ensuring compliance and security, this guide covers every aspect of operationalizing LLMs. Explore deployment strategies across platforms like AWS, Azure, GCP, and Hugging Face, learn about containerization and serverless architectures, and dive into tools for monitoring and observability such as Prometheus and Grafana. Through practical frameworks and case studies, the book provides actionable insights into managing performance metrics, addressing model drift, and leveraging distributed systems for scalability. Designed for data scientists, LLM engineers, and AI practitioners, The LLMOps Advantage also delves into ethical considerations, emerging trends like multi-modal models, and best practices for integrating LLMs with existing workflows. Whether you ' re fine-tuning models for specific tasks or scaling solutions to meet enterprise needs, this book equips you with the expertise to harness the full potential of LLMs. Stay ahead in the AI revolution with The LLMOps Advantage—your essential roadmap to mastering the future of large language model operations.
Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation
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Author : Phanish Lakkarasu
language : en
Publisher: Deep Science Publishing
Release Date : 2025-06-06
Designing Scalable And Intelligent Cloud Architectures An End To End Guide To Ai Driven Platforms Mlops Pipelines And Data Engineering For Digital Transformation written by Phanish Lakkarasu and has been published by Deep Science Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-06 with Computers categories.
In today’s fast-paced digital era, organizations are under constant pressure to innovate, scale, and deliver intelligent services with speed and reliability. Designing Scalable and Intelligent Cloud Architectures: An End-to-End Guide to AI-Driven Platforms, MLOps Pipelines, and Data Engineering for Digital Transformation is a comprehensive exploration into the foundational and advanced components required to build robust, future-ready cloud ecosystems. This book is the product of years of observing the shifting paradigms in enterprise IT—from legacy systems and monolithic architectures to microservices, serverless computing, and AI-powered infrastructure. At the heart of this evolution lies the need for cloud-native platforms that are not only scalable and resilient but also intelligent and automation-ready. The content in these pages is aimed at architects, engineers, data scientists, DevOps professionals, and digital transformation leaders who seek to understand and implement the key building blocks of modern cloud systems. It delves into the design principles behind scalable infrastructure, best practices for integrating AI and Machine Learning, and the implementation of MLOps pipelines to streamline deployment, monitoring, and continuous improvement of ML models. Furthermore, it provides practical insights into data engineering strategies that ensure secure, efficient, and real-time data flow across distributed environments. We also explore critical topics such as multi-cloud and hybrid cloud strategies, edge computing, observability, cost optimization, and governance—ensuring that readers are equipped to tackle both the technical and operational challenges of building next-generation platforms. What sets this book apart is its unified approach to cloud, AI, and data engineering—treating them not as isolated silos but as interconnected pillars of intelligent digital transformation. Whether you are designing enterprise-grade solutions or modernizing existing infrastructures, this guide will serve as your companion in navigating complexity with clarity and confidence.
Google Certification Guide Google Professional Data Engineer
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Author : Cybellium
language : en
Publisher: Cybellium Ltd
Release Date :
Google Certification Guide Google Professional Data Engineer written by Cybellium and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
Google Certification Guide - Google Professional Data Engineer Navigate the Data Landscape with Google Cloud Expertise Embark on a journey to become a Google Professional Data Engineer with this comprehensive guide. Tailored for data professionals seeking to leverage Google Cloud's powerful data solutions, this book provides a deep dive into the core concepts, practices, and tools necessary to excel in the field of data engineering. Inside, You'll Explore: Fundamentals to Advanced Data Concepts: Understand the full spectrum of Google Cloud data services, from BigQuery and Dataflow to AI and machine learning integrations. Practical Data Engineering Scenarios: Learn through hands-on examples and real-life case studies that demonstrate how to effectively implement data solutions on Google Cloud. Focused Exam Strategy: Prepare for the certification exam with detailed insights into the exam format, including key topics, study strategies, and practice questions. Current Trends and Best Practices: Stay abreast of the latest advancements in Google Cloud data technologies, ensuring your skills are up-to-date and industry-relevant. Authored by a Data Engineering Expert Written by an experienced data engineer, this guide bridges practical application with theoretical knowledge, offering a comprehensive and practical learning experience. Your Comprehensive Guide to Data Engineering Certification Whether you're an aspiring data engineer or an experienced professional looking to validate your Google Cloud skills, this book is an invaluable resource, guiding you through the nuances of data engineering on Google Cloud and preparing you for the Professional Data Engineer exam. Elevate Your Data Engineering Skills This guide is more than a certification prep book; it's a deep dive into the art of data engineering in the Google Cloud ecosystem, designed to equip you with advanced skills and knowledge for a successful career in data engineering. Begin Your Data Engineering Journey Step into the world of Google Cloud data engineering with confidence. This guide is your first step towards mastering the concepts and practices of data engineering and achieving certification as a Google Professional Data Engineer. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
Optimizing Data Pipelines With Azure Advanced Etl And Analytics Solutions For Modern Enterprises
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Author : Dinesh Nayak Banoth Afroz Shaik Prof. Sandeep Kumar
language : en
Publisher: DeepMisti Publication
Release Date : 2025-01-01
Optimizing Data Pipelines With Azure Advanced Etl And Analytics Solutions For Modern Enterprises written by Dinesh Nayak Banoth Afroz Shaik Prof. Sandeep Kumar and has been published by DeepMisti Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-01 with Computers categories.
In today’s fast-paced digital landscape, data has become one of the most valuable assets for organizations striving to gain a competitive edge. However, managing, processing, and extracting actionable insights from vast volumes of data has become increasingly complex. Traditional methods are no longer sufficient to handle the demands of modern enterprise systems, which require high-performance, scalable, and reliable data solutions. This book, Optimizing Data Pipelines with Azure: Advanced ETL and Analytics Solutions for Modern Enterprises, explores the intricacies of designing and optimizing data pipelines using Microsoft Azure’s powerful cloud ecosystem. Azure has emerged as a leader in providing scalable, flexible, and secure cloud solutions that help businesses streamline their data processing workflows, enhance analytics capabilities, and make data-driven decisions at scale. This book is designed to serve both as a comprehensive guide and a practical reference for professionals looking to leverage Azure’s advanced data engineering tools and technologies. Whether you are a data engineer, architect, or business intelligence professional, you will find practical insights and detailed instructions on how to implement end-to-end data pipelines on Azure. Throughout this book, we delve into key concepts such as Extract, Transform, Load (ETL) processes, data integration, real-time analytics, and the optimization of data workflows using Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and other leading Azure services. We will walk you through how to design flexible, reliable, and highly performant data pipelines tailored to the specific needs of modern enterprises. By the end of this book, you will have a clear understanding of how to efficiently manage large-scale data flows, optimize ETL processes, and implement robust analytics solutions on Azure to unlock valuable insights. Whether you're tackling data ingestion, processing, storage, or analytics, this book will equip you with the tools and strategies to succeed in the ever-evolving world of data engineering and analytics. I hope this book inspires and empowers you to transform how your organization handles its data and drives future success through advanced data pipeline optimization techniques. — Author