Author: Publisher:
ISBN: 1492038717
Category :
Languages : en
Pages : 189
Book Description
Foundations for Architecting Data Solutions
Author: Ted MalaskaPublisher: "O'Reilly Media, Inc."
ISBN: 1492038695
Category : Computers
Languages : en
Pages : 196
Book Description
While many companies ponder implementation details such as distributed processing engines and algorithms for data analysis, this practical book takes a much wider view of big data development, starting with initial planning and moving diligently toward execution. Authors Ted Malaska and Jonathan Seidman guide you through the major components necessary to start, architect, and develop successful big data projects. Everyone from CIOs and COOs to lead architects and developers will explore a variety of big data architectures and applications, from massive data pipelines to web-scale applications. Each chapter addresses a piece of the software development life cycle and identifies patterns to maximize long-term success throughout the life of your project. Start the planning process by considering the key data project types Use guidelines to evaluate and select data management solutions Reduce risk related to technology, your team, and vague requirements Explore system interface design using APIs, REST, and pub/sub systems Choose the right distributed storage system for your big data system Plan and implement metadata collections for your data architecture Use data pipelines to ensure data integrity from source to final storage Evaluate the attributes of various engines for processing the data you collect
Foundations of Data Intensive Applications
Author: Supun KamburugamuvePublisher: John Wiley & Sons
ISBN: 1119713013
Category : Computers
Languages : en
Pages : 416
Book Description
PEEK “UNDER THE HOOD” OF BIG DATA ANALYTICS The world of big data analytics grows ever more complex. And while many people can work superficially with specific frameworks, far fewer understand the fundamental principles of large-scale, distributed data processing systems and how they operate. In Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood, renowned big-data experts and computer scientists Drs. Supun Kamburugamuve and Saliya Ekanayake deliver a practical guide to applying the principles of big data to software development for optimal performance. The authors discuss foundational components of large-scale data systems and walk readers through the major software design decisions that define performance, application type, and usability. You???ll learn how to recognize problems in your applications resulting in performance and distributed operation issues, diagnose them, and effectively eliminate them by relying on the bedrock big data principles explained within. Moving beyond individual frameworks and APIs for data processing, this book unlocks the theoretical ideas that operate under the hood of every big data processing system. Ideal for data scientists, data architects, dev-ops engineers, and developers, Foundations of Data Intensive Applications: Large Scale Data Analytics under the Hood shows readers how to: Identify the foundations of large-scale, distributed data processing systems Make major software design decisions that optimize performance Diagnose performance problems and distributed operation issues Understand state-of-the-art research in big data Explain and use the major big data frameworks and understand what underpins them Use big data analytics in the real world to solve practical problems
Building Cloud Data Platforms Solutions
Author: Anouar BEN ZAHRAPublisher: Anouar BEN ZAHRA
ISBN:
Category : Computers
Languages : en
Pages : 339
Book Description
"Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud" comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects.
Data Architecture: A Primer for the Data Scientist
Author: W.H. InmonPublisher: Academic Press
ISBN: 0128169176
Category : Computers
Languages : en
Pages : 431
Book Description
Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture
Author: John ParkinsonData Architecture: A Primer for the Data Scientist
Author: W.H. InmonPublisher: Morgan Kaufmann
ISBN: 0128020911
Category : Computers
Languages : en
Pages : 378
Book Description
Today, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or architecture it can’t be used to its full potential. Data Architecture a Primer for the Data Scientist addresses the larger architectural picture of how Big Data fits with the existing information infrastructure, an essential topic for the data scientist. Drawing upon years of practical experience and using numerous examples and an easy to understand framework. W.H. Inmon, and Daniel Linstedt define the importance of data architecture and how it can be used effectively to harness big data within existing systems. You’ll be able to: Turn textual information into a form that can be analyzed by standard tools. Make the connection between analytics and Big Data Understand how Big Data fits within an existing systems environment Conduct analytics on repetitive and non-repetitive data Discusses the value in Big Data that is often overlooked, non-repetitive data, and why there is significant business value in using it Shows how to turn textual information into a form that can be analyzed by standard tools Explains how Big Data fits within an existing systems environment Presents new opportunities that are afforded by the advent of Big Data Demystifies the murky waters of repetitive and non-repetitive data in Big Data
Architecting Data-Driven Systems Book 2
Author: John KleppmannPublisher:
ISBN: 9781806218660
Category :
Languages : en
Pages : 0
Book Description
���� Mastering Data-Driven Systems: A Comprehensive Handbook Unearth the secrets of constructing cutting-edge data-driven systems with this holistic guide tailored for professionals, students, and enthusiasts alike. Here's why this book is the key to unlocking the next era of tech innovation: ���� Solid Foundations: Grasp the essentials with "Foundations of Data-Driven Architectures" and build on a rock-solid base. ���� Design Excellence: Dive deep into "Data Modeling and Design," and its advanced counterpart, setting the blueprint for efficient systems. ���� Next-Gen Storage: Not one, but two comprehensive chapters on modern and advanced storage solutions, ensuring your data is always ready and resilient. ���� Integration Mastery: With a dual focus on "Data Integration and Transformation," understand the magic of merging diverse datasets seamlessly. ���� Analytics Powerhouse: Unlock insights like never before with "Advanced Analytics Frameworks and Engines" and "Real-time Analytics and Stream Processing." ���� AI Synergy: Discover the groundbreaking "Confluence of Data and Artificial Intelligence (AI)," where data meets its most powerful ally. ���� Microservices Edge: Enhance flexibility with "System Scalability with Microservices," making your systems adaptable and agile. ���� Guarding Data: Dive into the intricacies of "Data Governance" with two focused chapters ensuring quality and trust in your data-driven endeavors. ���� Machine Learning Magic: Harness the prowess of "Advanced Machine Learning in Data-Driven Systems" for predictive and actionable insights. ���� All-Encompassing Integration: Unpack the complexities of "Multi-Modal Data Integration in System Design." ���� Reliability & Performance: Ensure your systems are always up, fast, and responsive with chapters on "Scalability" and "Performance Optimization." ���� Safety First: Safeguard your assets with "Advanced Data Security and Compliance." ���� Conclusion: Synthesize your newfound knowledge and gaze into the future of data-driven systems. Embrace the future with this handbook, and elevate your projects, designs, and understanding to newfound heights. ���� Secure your key to the future of data-driven systems! Dive in today. ����
Azure Modern Data Architecture
Author: Anouar BEN ZAHRAPublisher: Anouar BEN ZAHRA
ISBN:
Category : Computers
Languages : en
Pages : 319
Book Description
Key Features Discover the key drivers of successful Azure architecture Practical guidance Focus on scalability and performance Expert authorship Book Description This book presents a guide to design and implement scalable, secure, and efficient data solutions in the Azure cloud environment. It provides Data Architects, developers, and IT professionals who are responsible for designing and implementing data solutions in the Azure cloud environment with the knowledge and tools needed to design and implement data solutions using the latest Azure data services. It covers a wide range of topics, including data storage, data processing, data analysis, and data integration. In this book, you will learn how to select the appropriate Azure data services, design a data processing pipeline, implement real-time data processing, and implement advanced analytics using Azure Databricks and Azure Synapse Analytics. You will also learn how to implement data security and compliance, including data encryption, access control, and auditing. Whether you are building a new data architecture from scratch or migrating an existing on premises solution to Azure, the Azure Data Architecture Guidelines are an essential resource for any organization looking to harness the power of data in the cloud. With these guidelines, you will gain a deep understanding of the principles and best practices of Azure data architecture and be equipped to build data solutions that are highly scalable, secure, and cost effective. What You Need to Use this Book? To use this book, it is recommended that readers have a basic understanding of data architecture concepts and data management principles. Some familiarity with cloud computing and Azure services is also helpful. The book is designed for data architects, data engineers, data analysts, and anyone involved in designing, implementing, and managing data solutions on the Azure cloud platform. It is also suitable for students and professionals who want to learn about Azure data architecture and its best practices.
Author: William H. Inmon