Apache Hudi For Scalable Data Lakes

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Apache Hudi For Scalable Data Lakes

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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-07-24

Apache Hudi For Scalable Data Lakes written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-24 with Computers categories.

"Apache Hudi for Scalable Data Lakes" "Apache Hudi for Scalable Data Lakes" is a comprehensive guide designed for data engineers, architects, and technical leaders seeking to harness the full potential of modern data lakes. The book opens with an exploration of the core concepts and motivations behind distributed data lake architectures, offering detailed insights into the evolution of Apache Hudi within the broader open-source ecosystem. Readers are guided through Hudi’s foundational principles, comparative positioning alongside Delta Lake and Apache Iceberg, and the unique design goals that enable workloads such as incremental processing, change data capture (CDC), and transactional ingestion. Delving deep into implementation, the book meticulously covers Hudi’s innovative storage mechanisms, including Copy-on-Write and Merge-on-Read table types, schema evolution strategies, and metadata management. Successive chapters provide hands-on guidance for efficient data ingestion—both batch and streaming—while illuminating Hudi’s transactional guarantees, scalable indexing, and best practices for tuning write and read performance. Integration with leading query engines such as Trino, Hive, Presto, and Spark SQL is addressed in detail, alongside advanced topics like time travel queries, file management, and robust failure recovery techniques. Beyond technical architecture, the text provides pragmatic approaches to scaling Hudi deployments in cloud and hybrid environments, ensuring data reliability, consistency, and high performance even at petabyte scale. With dedicated discussions on security, governance, DevOps automation, and compliance—including audit logging, encryption, GDPR controls, and continuous data quality—the book empowers practitioners to build resilient, secure, and agile data lake platforms. The final chapters engage with cutting-edge developments, community-driven extensions, and the dynamic future of Apache Hudi, making this volume an essential resource for staying ahead in the rapidly evolving world of big data.

Mastering Apache Hudi

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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-06

Mastering Apache Hudi written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.

"Mastering Apache Hudi: Building Real-Time Data Lakes" is an authoritative guide designed to equip data engineers, architects, and IT professionals with the knowledge and skills needed to leverage Apache Hudi’s powerful capabilities in managing dynamic, continuously evolving datasets. As organizations worldwide strive to harness the vast streams of real-time data for actionable insights, this book demystifies the intricacies of deploying and optimizing Hudi, turning traditional data lakes into agile, real-time analytical engines. This comprehensive resource covers a spectrum of essential topics, from the architectural components underpinning Hudi’s functionality to practical strategies for seamless integration with existing big data ecosystems. Readers will gain invaluable insights into performance tuning, schema evolution, and data governance, alongside real-world case studies that highlight industry best practices and successful Hudi implementations. With step-by-step guidance and expert insights, this book empowers professionals to transform their data infrastructures, enabling rapid and informed decision-making in a data-driven world.

Applied Hudi Systems

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-03

Applied Hudi Systems written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-03 with Computers categories.

"Applied Hudi Systems" "Applied Hudi Systems" is a comprehensive and authoritative guide to architecting, operating, and optimizing Apache Hudi for modern, large-scale data lakes. The book begins with a thorough exploration of Hudi’s architectural foundations and design philosophy, clarifying core concepts such as table abstractions (Copy-on-Write vs. Merge-on-Read), metadata management, transactional guarantees, and integration with distributed storage systems like HDFS, S3, and GCS. Readers will come away with a deep understanding of Hudi’s unique approach to reliable data storage, time-travel queries, and its positioning relative to other leading lakehouse formats. The book progresses from foundational principles to advanced engineering, covering high-throughput data ingestion using real-time and micro-batch pipelines, mutation management (upserts, deletes), data validation, and change data capture integration. Practical chapters on query processing, indexing, partitioning, clustering, and fine-grained performance tuning provide real-world strategies for achieving scalable, low-latency analytics. Detailed treatments of storage layout, compaction, lifecycle management, and cost optimization empower practitioners to build resilient and efficient Hudi-based architectures suitable for petabyte-scale deployments. Recognizing the demands of enterprise data platforms, "Applied Hudi Systems" addresses mission-critical topics such as security, governance, auditing, multi-tenancy, and disaster recovery. Readers will find comprehensive guidance on monitoring, telemetry, alerting, resource management, and extensibility with today’s data ecosystem tools (e.g., Spark, Trino, Airflow, Prometheus). The book culminates with best practices, operational playbooks, benchmark results, and in-depth case studies from production Hudi environments—making it an indispensable resource for engineers, architects, and data leaders seeking to deploy robust, future-ready data lake solutions.

Efficient Data Processing With Apache Pig

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-17

Efficient Data Processing With Apache Pig written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-17 with Computers categories.

"Efficient Data Processing with Apache Pig" Efficient Data Processing with Apache Pig is the definitive guide to mastering high-performance data transformation and pipeline design in today’s complex big data landscape. The book opens with a thorough examination of Apache Pig’s evolution, architectural foundations, and its crucial role within distributed data ecosystems. Readers gain a strategic perspective on where Pig excels compared to frameworks like MapReduce, Hive, and Spark, alongside practical guidance for deploying robust, enterprise-grade environments that prioritize scalability, multi-tenancy, and production resilience. Spanning fundamental data modeling practices, advanced Pig Latin techniques, and deep dives into resource optimization, this book is tailored for engineers, architects, and data professionals seeking practical strategies for building efficient, reliable pipelines. Each chapter balances conceptual clarity with technical depth—exploring schema evolution, advanced joins, aggregation patterns, modular scripting, and the intricacies of performance tuning. Readers also benefit from comprehensive coverage of extending Pig with custom UDFs, integrating with external data sources, and the nuances of workflow orchestration across Oozie, Airflow, and cloud-native platforms. The book moves beyond code and configuration, addressing critical considerations in security, compliance, and data governance—from authentication and encryption to auditing and lifecycle management. It concludes with actionable frameworks for migration, modernization, and hybrid architectures, coupled with future-focused discussions on AI integration, the evolving open-source ecosystem, and innovative real-world use cases at scale. Efficient Data Processing with Apache Pig is both a practical reference and an indispensable roadmap for leveraging Pig to its full potential in modern data environments.

Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025

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Author : Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Cloud First Data Engineering Architecting Scalable Pipelines And Analytics With Aws 2025 written by Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA 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.

Author:1- PEEYUSH PATEL Author:2 -DR. MANMOHAN SHARMA ISBN - 978-93-6788-817-9 Preface In today’s digital economy, organizations generate more data in a single day than many legacy systems could process in years. The shift to cloud-first architectures has transformed how we collect, store, and analyze information—enabling businesses to respond faster to market changes, scale without upfront hardware investments, and foster innovation across teams. This book, Cloud-First Data Engineering: Architecting Scalable Pipelines and Analytics with AWS, is written for data engineers, architects, and technical leaders who seek to design robust, high-performing data platforms using Amazon Web Services. Over the past decade, AWS has introduced a rich portfolio of data services—ranging from serverless ETL (AWS Glue) and streaming solutions (Kinesis, MSK) to petabyte-scale analytics (Redshift, Athena) and machine learning integrations (SageMaker). Yet, with such breadth comes complexity: selecting the right components, designing for cost efficiency, maintaining security and compliance, and ensuring operational excellence are constant challenges. This book distills best practices, architectural patterns, and real-world examples into a cohesive roadmap. You will learn how to build end-to-end pipelines that evolve with your data volume, implement modern data Lakehouse strategies, enable real-time insights, and incorporate governance at every layer. Chapters progress from foundational concepts—such as cloud-first paradigms and core AWS data services—to advanced topics like Data Mesh, serverless Lakehouse’s, generative AI for data quality, and emerging roles in data organization. Each section demystifies the trade-offs, illustrates implementation steps, and highlights pitfalls to avoid. Whether you are migrating legacy workloads, optimizing existing pipelines, or pioneering new analytics capabilities, this book serves as both a practical guide and strategic playbook to navigate the ever-changing landscape of cloud data engineering on AWS. Authors

The Cloud Data Lake

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Author : Rukmani Gopalan
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-12-12

The Cloud Data Lake written by Rukmani Gopalan 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 2022-12-12 with Computers categories.

More organizations than ever understand the importance of data lake architectures for deriving value from their data. Building a robust, scalable, and performant data lake remains a complex proposition, however, with a buffet of tools and options that need to work together to provide a seamless end-to-end pipeline from data to insights. This book provides a concise yet comprehensive overview on the setup, management, and governance of a cloud data lake. Author Rukmani Gopalan, a product management leader and data enthusiast, guides data architects and engineers through the major aspects of working with a cloud data lake, from design considerations and best practices to data format optimizations, performance optimization, cost management, and governance. Learn the benefits of a cloud-based big data strategy for your organization Get guidance and best practices for designing performant and scalable data lakes Examine architecture and design choices, and data governance principles and strategies Build a data strategy that scales as your organizational and business needs increase Implement a scalable data lake in the cloud Use cloud-based advanced analytics to gain more value from your data

Ultimate Big Data Analytics With Apache Hadoop Master Big Data Analytics With Apache Hadoop Using Apache Spark Hive And Python

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Author : Simhadri Govindappa
language : en
Publisher: Orange Education Pvt Limited
Release Date : 2024-09-09

Ultimate Big Data Analytics With Apache Hadoop Master Big Data Analytics With Apache Hadoop Using Apache Spark Hive And Python written by Simhadri Govindappa and has been published by Orange Education Pvt Limited this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-09 with Computers categories.

Master the Hadoop Ecosystem and Build Scalable Analytics Systems Key Features● Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ● Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ● Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics. Book Description In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape. The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises. You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python. Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively. Don’t miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop. What you will learn ● Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ● Master real-time analytics and data processing with Apache Spark’s powerful features. ● Develop skills in using Apache Hive for efficient data warehousing and complex queries. ● Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ● Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ● Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ● Build and deploy machine learning models with tools integrated into the Hadoop ecosystem. Table of Contents 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index

Rivery Workflow Design And Automation

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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-06-12

Rivery Workflow Design And Automation written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-12 with Computers categories.

"Rivery Workflow Design and Automation" "Rivery Workflow Design and Automation" is the definitive guide for data and DevOps professionals seeking to master modern workflow automation in the Rivery platform. Beginning with the foundational principles of workflow automation and Rivery’s unique position in the data engineering ecosystem, the book systematically unveils essential concepts including modular design, secure integration, and orchestration strategies. Readers are introduced to Rivery’s architecture, composable workflow structures, and industry-standard security considerations, setting a robust groundwork for the advanced techniques that follow. Delving deeper, the book progresses through advanced river implementations, sophisticated orchestration patterns, and scalable data operations tailored for real-world complexities. Detailed chapters provide actionable patterns for dynamic parameterization, error handling, transaction control, and lifecycle management within Rivery pipelines. The intricacies of both streaming and batch processing are explored, alongside data quality assurance and auditability, ensuring that practitioners can build reliable, compliant, and high-performing data workflows. To round out the practitioner's toolkit, "Rivery Workflow Design and Automation" addresses operational excellence with chapters on DevOps integration, infrastructure as code, continuous delivery, and cost optimization. Comprehensive coverage of security, governance, and external platform integration prepares readers for enterprise-scale automation challenges. With practical case studies, future-facing insights on AI-driven orchestration, and best practices distilled from industry deployments, this book is an essential companion for unlocking the full capabilities of Rivery and achieving scalable, resilient data automation.

Modern Data Architecture On Aws

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Author : Behram Irani
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-08-31

Modern Data Architecture On Aws written by Behram Irani 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 2023-08-31 with Computers categories.

Discover all the essential design and architectural patterns in one place to help you rapidly build and deploy your modern data platform using AWS services Key Features Learn to build modern data platforms on AWS using data lakes and purpose-built data services Uncover methods of applying security and governance across your data platform built on AWS Find out how to operationalize and optimize your data platform on AWS Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMany IT leaders and professionals are adept at extracting data from a particular type of database and deriving value from it. However, designing and implementing an enterprise-wide holistic data platform with purpose-built data services, all seamlessly working in tandem with the least amount of manual intervention, still poses a challenge. This book will help you explore end-to-end solutions to common data, analytics, and AI/ML use cases by leveraging AWS services. The chapters systematically take you through all the building blocks of a modern data platform, including data lakes, data warehouses, data ingestion patterns, data consumption patterns, data governance, and AI/ML patterns. Using real-world use cases, each chapter highlights the features and functionalities of numerous AWS services to enable you to create a scalable, flexible, performant, and cost-effective modern data platform. By the end of this book, you’ll be equipped with all the necessary architectural patterns and be able to apply this knowledge to efficiently build a modern data platform for your organization using AWS services.What you will learn Familiarize yourself with the building blocks of modern data architecture on AWS Discover how to create an end-to-end data platform on AWS Design data architectures for your own use cases using AWS services Ingest data from disparate sources into target data stores on AWS Build data pipelines, data sharing mechanisms, and data consumption patterns using AWS services Find out how to implement data governance using AWS services Who this book is for This book is for data architects, data engineers, and professionals creating data platforms. The book's use case–driven approach helps you conceptualize possible solutions to specific use cases, while also providing you with design patterns to build data platforms for any organization. It's beneficial for technical leaders and decision makers to understand their organization's data architecture and how each platform component serves business needs. A basic understanding of data & analytics architectures and systems is desirable along with beginner’s level understanding of AWS Cloud.

Cloud Native Financial Data Engineering Principles Pipelines And Scalable Architectures 2025

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Author : Author1:- ANOOP PURUSHOTAMAN, Author2:- PROF. DR M K SHARMA
language : en
Publisher: YASHITA PRAKASHAN PRIVATE LIMITED
Release Date :

Cloud Native Financial Data Engineering Principles Pipelines And Scalable Architectures 2025 written by Author1:- ANOOP PURUSHOTAMAN, Author2:- PROF. DR M K SHARMA 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 financial services industry has undergone a profound transformation over the past decade. From high-frequency trading firms demanding millisecond-level insights to retail banks seeking richer, personalized customer analytics, the scale, velocity, and variety of financial data have exploded. Traditional on-premises data warehouses and batch-oriented ETL pipelines struggle to keep pace with today’s requirements for real-time risk monitoring, fraud detection, algorithmic trading signals, and regulatory reporting. In parallel, the rise of cloud computing has unlocked virtually unlimited storage and compute capacity, democratized access to sophisticated analytics tools, and fostered an ecosystem of serverless and managed services designed for elasticity and resilience. This book, Cloud-Native Financial Data Engineering: Principles, Pipelines, and Scalable Architectures, is born out of the need to bridge these trends. It is written for data engineers, architects, and technology leaders who are tasked with designing and operating the next generation of financial data platforms. Whether you are building a streaming pipeline to ingest market quotes, an event-driven system to detect anomalous trading patterns, or a unified data lake that brings together transaction, customer, and risk data, the cloud offers a paradigm shift: you can focus on business logic and analytical value, rather than on undifferentiated heavy lifting of infrastructure. In the chapters that follow, we first establish the foundational principles of cloud-native data engineering in a financial context. We examine how to decompose monolithic ETL workflows into micro-services and pipelines, how to embrace immutable, append-only event stores, and how to design for failure and recovery at every layer. We then explore the core building blocks of modern data architecture: data ingestion patterns (batch, stream, change-data capture), transformation frameworks (serverless functions, containerized jobs, SQL-on-data-lake), metadata management, and orchestration engines. Along the way, we emphasize best practices for security, governance, and cost optimization—imperatives in a regulated, risk-averse industry. Subsequent sections dive into specialized topics that address the unique demands of financial workloads. We cover real-time analytics use cases such as market data enrichment, fraud-signal propagation, and credit-scoring model deployment. We unpack architectural patterns for high-throughput, low-latency pipelines—leveraging managed streaming platforms, serverless compute, column-arithmetic engines, and cloud-native message buses. We also address data quality and lineage at scale, showing how to embed continuous validation tests and visibility into every pipeline stage, thereby ensuring that trading strategies and risk models rest on a bedrock of trusted data. A recurring theme throughout this book is scalability: both horizontal scalability of compute and storage, and organizational scalability via self-service data platforms. We explore how to enable “data as a product” within your enterprise—providing domain teams with curated, discoverable datasets, APIs, and developer tooling so they can build analytics and machine-learning solutions without reinventing ingestion pipelines or wrestling with infrastructure details. This shift not only accelerates time to insight but also frees centralized engineering teams to focus on platform reliability, cost governance, and feature innovation. By combining conceptual frameworks with concrete, provider-agnostic examples, this book aims to be both a roadmap and a practical guide. Wherever possible, we illustrate patterns with code snippets and architectural diagrams, while also pointing to managed services offered by leading cloud providers. We encourage you to adapt these patterns to your organization’s existing standards and to rigorously validate them within your security and compliance constraints. As the lines between “finance” and “technology” continue to blur, the ability to engineer data pipelines that are resilient, elastic, and observably sound becomes a strategic differentiator. Whether you are modernizing a legacy data warehouse, building a next-gen risk platform, or architecting a real-time trading analytics engine, the cloud-native principles and patterns in this volume will equip you to deliver robust, cost-effective solutions that meet the exact demands of financial markets and regulatory bodies alike. We extend our gratitude to the practitioners, open-source contributors, and early adopters whose insights and feedback have shaped this book. It is our hope that by sharing these learnings, we collectively raise the bar for financial data engineering and help usher in an era where data-driven decisions can be made with confidence, speed, and scale. Authors