Engineering Data Mesh In Azure Cloud
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Engineering Data Mesh in Azure Cloud
Author: Aniruddha Deswandikar
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-03-29
Overcome data mesh adoption challenges using the cloud-scale analytics framework and make your data analytics landscape agile and efficient by using standard architecture patterns for diverse analytical workloads Key Features Delve into core data mesh concepts and apply them to real-world situations Safely reassess and redesign your framework for seamless data mesh integration Conquer practical challenges, from domain organization to building data contracts Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDecentralizing data and centralizing governance are practical, scalable, and modern approaches to data analytics. However, implementing a data mesh can feel like changing the engine of a moving car. Most organizations struggle to start and get caught up in the concept of data domains, spending months trying to organize domains. This is where Engineering Data Mesh in Azure Cloud can help. The book starts by assessing your existing framework before helping you architect a practical design. As you progress, you’ll focus on the Microsoft Cloud Adoption Framework for Azure and the cloud-scale analytics framework, which will help you quickly set up a landing zone for your data mesh in the cloud. The book also resolves common challenges related to the adoption and implementation of a data mesh faced by real customers. It touches on the concepts of data contracts and helps you build practical data contracts that work for your organization. The last part of the book covers some common architecture patterns used for modern analytics frameworks such as artificial intelligence (AI). By the end of this book, you’ll be able to transform existing analytics frameworks into a streamlined data mesh using Microsoft Azure, thereby navigating challenges and implementing advanced architecture patterns for modern analytics workloads.What you will learn Build a strategy to implement a data mesh in Azure Cloud Plan your data mesh journey to build a collaborative analytics platform Address challenges in designing, building, and managing data contracts Get to grips with monitoring and governing a data mesh Understand how to build a self-service portal for analytics Design and implement a secure data mesh architecture Resolve practical challenges related to data mesh adoption Who this book is for This book is for chief data officers and data architects of large and medium-size organizations who are struggling to maintain silos of data and analytics projects. Data architects and data engineers looking to understand data mesh and how it can help their organizations democratize data and analytics will also benefit from this book. Prior knowledge of managing centralized analytical systems, as well as experience with building data lakes, data warehouses, data pipelines, data integrations, and transformations is needed to get the most out of this book.
Mastering Big Data Engineering: AWS, GCP, & Azure Showdown
Author: Muthuraman Saminathan
language: en
Publisher: Libertatem Media Private Limited
Release Date: 2024-02-16
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.
Oracle Data Integrator Essentials
"Oracle Data Integrator Essentials" "Oracle Data Integrator Essentials" presents a comprehensive and authoritative guide to mastering Oracle's premier data integration platform. Organized into carefully structured chapters, this book covers foundational architecture, advanced configuration, metadata management, and integration best practices, offering readers a holistic understanding of both core principles and nuanced implementation strategies. From the building blocks of ODI Studio, agents, and repositories, to high-availability deployments and seamless integration with Oracle and third-party systems, the content is tailored to equip integration professionals, architects, and engineering teams with the expertise needed to leverage ODI's full capabilities. Delving deeply into practical application, the book explores advanced topics such as real-time and batch data flows, complex transformation patterns, reusable component design, and granular security controls. Readers will find step-by-step guidance on optimizing mappings, designing powerful Knowledge Modules, implementing robust change data capture, and ensuring regulatory compliance across multi-cloud and hybrid environments. Coverage of automation, DevOps practices, and lifecycle management demonstrates how modern data teams can continuously evolve their pipelines while maintaining operational excellence and governance. Addressing both current and future challenges, "Oracle Data Integrator Essentials" reviews the latest trends in data integration, including cloud-native architectures, data lakes, AI/ML pipelines, and DataOps. The book culminates in expert insights on troubleshooting, system modernization, migration paths, and aligning ODI with cutting-edge technologies in big data, streaming, and intelligent automation. Whether you are embarking on a new ODI implementation or modernizing existing platforms, this essential reference ensures readers are equipped to architect, secure, and optimize data integration solutions for today's enterprise demands.