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Author: RAVI KIRAN PAGIDI PROF.(DR.) VISHWADEEPAK SINGH BAGHELA
Publisher: DeepMisti Publication
ISBN: 9360443239
Category : Computers
Languages : en
Pages : 186
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Book Description
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
Author: RAVI KIRAN PAGIDI PROF.(DR.) VISHWADEEPAK SINGH BAGHELA
Publisher: DeepMisti Publication
ISBN: 9360443239
Category : Computers
Languages : en
Pages : 186
Get Book Here
Book Description
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
Author: Adi Wijaya
Publisher: Packt Publishing Ltd
ISBN: 1800565062
Category : Computers
Languages : en
Pages : 440
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Book Description
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.
Author: Manoj Kukreja
Publisher: Packt Publishing Ltd
ISBN: 1801074321
Category : Computers
Languages : en
Pages : 480
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Book Description
Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected.
Author: AKASH BALAJI MALI PROF. (DR.) SUDEEPT SINGH YADAV
Publisher: DeepMisti Publication
ISBN: 9360442577
Category : Computers
Languages : en
Pages : 196
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Book Description
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
Author: Sreeram Nudurupati
Publisher: Packt Publishing Ltd
ISBN: 1800563094
Category : Computers
Languages : en
Pages : 322
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Book Description
Get started with distributed computing using PySpark, a single unified framework to solve end-to-end data analytics at scale Key FeaturesDiscover how to convert huge amounts of raw data into meaningful and actionable insightsUse Spark's unified analytics engine for end-to-end analytics, from data preparation to predictive analyticsPerform data ingestion, cleansing, and integration for ML, data analytics, and data visualizationBook Description Apache Spark is a unified data analytics engine designed to process huge volumes of data quickly and efficiently. PySpark is Apache Spark's Python language API, which offers Python developers an easy-to-use scalable data analytics framework. Essential PySpark for Scalable Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of Apache Spark. You'll begin your analytics journey with the data engineering process, learning how to perform data ingestion, cleansing, and integration at scale. This book helps you build real-time analytics pipelines that help you gain insights faster. You'll then discover methods for building cloud-based data lakes, and explore Delta Lake, which brings reliability to data lakes. The book also covers Data Lakehouse, an emerging paradigm, which combines the structure and performance of a data warehouse with the scalability of cloud-based data lakes. Later, you'll perform scalable data science and machine learning tasks using PySpark, such as data preparation, feature engineering, and model training and productionization. Finally, you'll learn ways to scale out standard Python ML libraries along with a new pandas API on top of PySpark called Koalas. By the end of this PySpark book, you'll be able to harness the power of PySpark to solve business problems. What you will learnUnderstand the role of distributed computing in the world of big dataGain an appreciation for Apache Spark as the de facto go-to for big data processingScale out your data analytics process using Apache SparkBuild data pipelines using data lakes, and perform data visualization with PySpark and Spark SQLLeverage the cloud to build truly scalable and real-time data analytics applicationsExplore the applications of data science and scalable machine learning with PySparkIntegrate your clean and curated data with BI and SQL analysis toolsWho this book is for This book is for practicing data engineers, data scientists, data analysts, and data enthusiasts who are already using data analytics to explore distributed and scalable data analytics. Basic to intermediate knowledge of the disciplines of data engineering, data science, and SQL analytics is expected. General proficiency in using any programming language, especially Python, and working knowledge of performing data analytics using frameworks such as pandas and SQL will help you to get the most out of this book.
Author: Nabendu Chaki
Publisher: Springer
ISBN: 9811063192
Category : Technology & Engineering
Languages : en
Pages : 376
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Book Description
The book presents high quality research work in cutting edge technologies and most-happening areas of computational intelligence and data engineering. It contains selected papers presented at International Conference on Computational Intelligence and Data Engineering (ICCIDE 2017). The conference was conceived as a forum for presenting and exchanging ideas and results of the researchers from academia and industry onto a common platform and help them develop a comprehensive understanding of the challenges of technological advancements from different viewpoints. This book will help in fostering a healthy and vibrant relationship between academia and industry. The topics of the conference include, but are not limited to collective intelligence, intelligent transportation systems, fuzzy systems, Bayesian network, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, and speech processing.
Author: Mayank Malhotra
Publisher: Orange Education Pvt Ltd
ISBN: 8196994788
Category : Computers
Languages : en
Pages : 280
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Book Description
Navigating Databricks with Ease for Unparalleled Data Engineering Insights. KEY FEATURES ● Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. ● Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. ● Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks. DESCRIPTION Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike. Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions. Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as data engineering professionals in today's competitive job market. WHAT WILL YOU LEARN ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines. ● Design and implement high-performance data solutions for scalability. ● Apply essential best practices for ensuring data integrity in pipelines. ● Explore advanced Databricks features for tackling complex data tasks. ● Learn to optimize data pipelines for streamlined workflows. WHO IS THIS BOOK FOR? This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends. A basic understanding of data engineering concepts and familiarity with cloud computing will enhance the learning experience. TABLE OF CONTENTS 1. Fundamentals of Data Engineering 2. Mastering Delta Tables in Databricks 3. Data Ingestion and Extraction 4. Data Transformation and ETL Processes 5. Data Quality and Validation 6. Data Modeling and Storage 7. Data Orchestration and Workflow Management 8. Performance Tuning and Optimization 9. Scalability and Deployment Considerations 10. Data Security and Governance Last Words Index
Author: Cybellium
Publisher: Cybellium Ltd
ISBN: 1836798032
Category : Computers
Languages : en
Pages : 228
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Book Description
Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
Author: Ashish Agarwal
Publisher: Orange Education Pvt Ltd
ISBN: 8197651140
Category : Computers
Languages : en
Pages : 297
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Book Description
TAGLINE Discover the world of data engineering in an on-premises setting versus the Azure cloud KEY FEATURES ● Explore Azure data engineering from foundational concepts to advanced techniques, spanning SQL databases, ETL processes, and cloud-native solutions. ● Learn to implement real-world data projects with Azure services, covering data integration, storage, and analytics, tailored for diverse business needs. ● Prepare effectively for Azure data engineering certifications with detailed exam-focused content and practical exercises to reinforce learning. DESCRIPTION Embark on a comprehensive journey into Azure data engineering with “Ultimate Azure Data Engineering”. Starting with foundational topics like SQL and relational database concepts, you'll progress to comparing data engineering practices in Azure versus on-premises environments. Next, you will dive deep into Azure cloud fundamentals, learning how to effectively manage heterogeneous data sources and implement robust Extract, Transform, Load (ETL) concepts using Azure Data Factory, mastering the orchestration of data workflows and pipeline automation. The book then moves to explore advanced database design strategies and discover best practices for optimizing data performance and ensuring stringent data security measures. You will learn to visualize data insights using Power BI and apply these skills to real-world scenarios. Whether you're aiming to excel in your current role or preparing for Azure data engineering certifications, this book equips you with practical knowledge and hands-on expertise to thrive in the dynamic field of Azure data engineering. WHAT WILL YOU LEARN ● Master the core principles and methodologies that drive data engineering such as data processing, storage, and management techniques. ● Gain a deep understanding of Structured Query Language (SQL) and relational database management systems (RDBMS) for Azure Data Engineering. ● Learn about Azure cloud services for data engineering, such as Azure SQL Database, Azure Data Factory, Azure Synapse Analytics, and Azure Blob Storage. ● Gain proficiency to orchestrate data workflows, schedule data pipelines, and monitor data integration processes across cloud and hybrid environments. ● Design optimized database structures and data models tailored for performance and scalability in Azure. ● Implement techniques to optimize data performance such as query optimization, caching strategies, and resource utilization monitoring. ● Learn how to visualize data insights effectively using tools like Power BI to create interactive dashboards and derive data-driven insights. ● Equip yourself with the knowledge and skills needed to pass Microsoft Azure data engineering certifications. WHO IS THIS BOOK FOR? This book is tailored for a diverse audience including aspiring and current Azure data engineers, data analysts, and data scientists, along with database and BI developers, administrators, and analysts. It is an invaluable resource for those aiming to obtain Azure data engineering certifications. TABLE OF CONTENTS 1. Introduction to Data Engineering 2. Understanding SQL and RDBMS Concepts 3. Data Engineering: Azure Versus On-Premises 4. Azure Cloud Concepts 5. Working with Heterogenous Data Sources 6. ETL Concepts 7. Database Design and Modeling 8. Performance Best Practices and Data Security 9. Data Visualization and Application in Real World 10. Data Engineering Certification Guide Index
Author: Ron C. L'Esteve
Publisher: Apress
ISBN: 9781484271810
Category : Computers
Languages : en
Pages : 612
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Book Description
Build efficient and scalable batch and real-time data ingestion pipelines, DevOps continuous integration and deployment pipelines, and advanced analytics solutions on the Azure Data Platform. This book teaches you to design and implement robust data engineering solutions using Data Factory, Databricks, Synapse Analytics, Snowflake, Azure SQL database, Stream Analytics, Cosmos database, and Data Lake Storage Gen2. You will learn how to engineer your use of these Azure Data Platform components for optimal performance and scalability. You will also learn to design self-service capabilities to maintain and drive the pipelines and your workloads. The approach in this book is to guide you through a hands-on, scenario-based learning process that will empower you to promote digital innovation best practices while you work through your organization’s projects, challenges, and needs. The clear examples enable you to use this book as a reference and guide for building data engineering solutions in Azure. After reading this book, you will have a far stronger skill set and confidence level in getting hands on with the Azure Data Platform. What You Will Learn Build dynamic, parameterized ELT data ingestion orchestration pipelines in Azure Data Factory Create data ingestion pipelines that integrate control tables for self-service ELT Implement a reusable logging framework that can be applied to multiple pipelines Integrate Azure Data Factory pipelines with a variety of Azure data sources and tools Transform data with Mapping Data Flows in Azure Data Factory Apply Azure DevOps continuous integration and deployment practices to your Azure Data Factory pipelines and development SQL databases Design and implement real-time streaming and advanced analytics solutions using Databricks, Stream Analytics, and Synapse Analytics Get started with a variety of Azure data services through hands-on examples Who This Book Is For Data engineers and data architects who are interested in learning architectural and engineering best practices around ELT and ETL on the Azure Data Platform, those who are creating complex Azure data engineering projects and are searching for patterns of success, and aspiring cloud and data professionals involved in data engineering, data governance, continuous integration and deployment of DevOps practices, and advanced analytics who want a full understanding of the many different tools and technologies that Azure Data Platform provides
