Download or read book Modern Data Strategy written by Mike Fleckenstein and published by Springer. This book was released on 2018-02-12 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management.

Download or read book Architecting Modern Data Platforms written by Jan Kunigk and published by "O'Reilly Media, Inc.". This book was released on 2018-12-05 with total page 636 pages. Available in PDF, EPUB and Kindle. Book excerpt: There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability

Download or read book Modern Data Analysis written by Robert L. Launer and published by Academic Press. This book was released on 2014-05-12 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Data Analysis contains the proceedings of a Workshop on Modern Data Analysis held in Raleigh, North Carolina, on June 2-4, 1980 under the auspices of the United States Army Research Office. The papers review theories and methods of data analysis and cover topics ranging from single and multiple quantile-quantile (Q-Q) plotting procedures to biplot display and pencil-and-paper exploratory data analysis methods. Projection pursuit methods for data analysis are also discussed. Comprised of nine chapters, this book begins with an introduction to styles of data analysis techniques, followed by an analysis of single and multiple Q-Q plotting procedures. Problems involving extreme-value data and the behavior of sample averages are considered. Subsequent chapters deal with the use of smelting in guiding re-expression; geometric data analysis; and influence functions and regression diagnostics. The final chapter examines the use and interpretation of robust analysis of variance for the general non-full-rank linear model. The procedures are described in terms of their mathematical structure, which leads to efficient computational algorithms. This monograph should be of interest to mathematicians and statisticians.

Download or read book Modern Big Data Architectures written by Dominik Ryzko and published by John Wiley & Sons. This book was released on 2020-03-31 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an up-to-date analysis of big data and multi-agent systems The term Big Data refers to the cases, where data sets are too large or too complex for traditional data-processing software. With the spread of new concepts such as Edge Computing or the Internet of Things, production, processing and consumption of this data becomes more and more distributed. As a result, applications increasingly require multiple agents that can work together. A multi-agent system (MAS) is a self-organized computer system that comprises multiple intelligent agents interacting to solve problems that are beyond the capacities of individual agents. Modern Big Data Architectures examines modern concepts and architecture for Big Data processing and analytics. This unique, up-to-date volume provides joint analysis of big data and multi-agent systems, with emphasis on distributed, intelligent processing of very large data sets. Each chapter contains practical examples and detailed solutions suitable for a wide variety of applications. The author, an internationally-recognized expert in Big Data and distributed Artificial Intelligence, demonstrates how base concepts such as agent, actor, and micro-service have reached a point of convergence—enabling next generation systems to be built by incorporating the best aspects of the field. This book: Illustrates how data sets are produced and how they can be utilized in various areas of industry and science Explains how to apply common computational models and state-of-the-art architectures to process Big Data tasks Discusses current and emerging Big Data applications of Artificial Intelligence Modern Big Data Architectures: A Multi-Agent Systems Perspective is a timely and important resource for data science professionals and students involved in Big Data analytics, and machine and artificial learning.

Download or read book Mastering the Modern Data Stack written by Nick Jewell, PhD and published by TinyTechMedia LLC. This book was released on 2023-09-28 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the age of digital transformation, becoming overwhelmed by the sheer volume of potential data management, analytics, and AI solutions is common. Then it's all too easy to become distracted by glossy vendor marketing, and then chase the latest shiny tool, rather than focusing on building resilient, valuable platforms that will outperform the competition. This book aims to fix a glaring gap for data professionals: a comprehensive guide to the full Modern Data Stack that's rooted in real-world capabilities, not vendor hype. It is full of hard-earned advice on how to get maximum value from your investments through tangible insights, actionable strategies, and proven best practices. It comprehensively explains how the Modern Data Stack is truly utilized by today's data-driven companies. Mastering the Modern Data Stack: An Executive Guide to Unified Business Analytics is crafted for a diverse audience. It's for business and technology leaders who understand the importance and potential value of data, analytics, and AI—but don’t quite see how it all fits together in the big picture. It's for enterprise architects and technology professionals looking for a primer on the data analytics domain, including definitions of essential components and their usage patterns. It's also for individuals early in their data analytics careers who wish to have a practical and jargon-free understanding of how all the gears and pulleys move behind the scenes in a Modern Data Stack to turn data into actual business value. Whether you're starting your data journey with modest resources, or implementing digital transformation in the cloud, you'll find that this isn't just another textbook on data tools or a mere overview of outdated systems. It's a powerful guide to efficient, modern data management and analytics, with a firm focus on emerging technologies such as data science, machine learning, and AI. If you want to gain a competitive advantage in today’s fast-paced digital world, this TinyTechGuide™ is for you. Remember, it’s not the tech that’s tiny, just the book!™

Download or read book Data Management at Scale written by Piethein Strengholt and published by "O'Reilly Media, Inc.". This book was released on 2020-07-29 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata

Download or read book Modern Big Data Processing with Hadoop written by V Naresh Kumar and published by Packt Publishing Ltd. This book was released on 2018-03-30 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive guide to design, build and execute effective Big Data strategies using Hadoop Key Features -Get an in-depth view of the Apache Hadoop ecosystem and an overview of the architectural patterns pertaining to the popular Big Data platform -Conquer different data processing and analytics challenges using a multitude of tools such as Apache Spark, Elasticsearch, Tableau and more -A comprehensive, step-by-step guide that will teach you everything you need to know, to be an expert Hadoop Architect Book Description The complex structure of data these days requires sophisticated solutions for data transformation, to make the information more accessible to the users.This book empowers you to build such solutions with relative ease with the help of Apache Hadoop, along with a host of other Big Data tools. This book will give you a complete understanding of the data lifecycle management with Hadoop, followed by modeling of structured and unstructured data in Hadoop. It will also show you how to design real-time streaming pipelines by leveraging tools such as Apache Spark, and build efficient enterprise search solutions using Elasticsearch. You will learn to build enterprise-grade analytics solutions on Hadoop, and how to visualize your data using tools such as Apache Superset. This book also covers techniques for deploying your Big Data solutions on the cloud Apache Ambari, as well as expert techniques for managing and administering your Hadoop cluster. By the end of this book, you will have all the knowledge you need to build expert Big Data systems. What you will learn Build an efficient enterprise Big Data strategy centered around Apache Hadoop Gain a thorough understanding of using Hadoop with various Big Data frameworks such as Apache Spark, Elasticsearch and more Set up and deploy your Big Data environment on premises or on the cloud with Apache Ambari Design effective streaming data pipelines and build your own enterprise search solutions Utilize the historical data to build your analytics solutions and visualize them using popular tools such as Apache Superset Plan, set up and administer your Hadoop cluster efficiently Who this book is for This book is for Big Data professionals who want to fast-track their career in the Hadoop industry and become an expert Big Data architect. Project managers and mainframe professionals looking forward to build a career in Big Data Hadoop will also find this book to be useful. Some understanding of Hadoop is required to get the best out of this book.

Download or read book Modern Data Protection written by W. Curtis Preston and published by "O'Reilly Media, Inc.". This book was released on 2021-04-29 with total page 386 pages. Available in PDF, EPUB and Kindle. Book excerpt: Give your organization the data protection it deserves without the uncertainty and cost overruns experienced by your predecessors or other companies. System and network administrators have their work cut out for them to protect physical and virtual machines in the data center and the cloud; mobile devices including laptops and tablets; SaaS services like Microsoft 365, Google Workspace, and Salesforce; and persistent data created by Kubernetes and container workloads. To help you navigate the breadth and depth of this challenge, this book presents several solutions so you can determine which is right for your company. You'll learn the unique requirements that each workload presents, then explore various categories of commercial backup hardware, software, and services available to protect these data sources, including the advantages and disadvantages of each approach. Learn the workload types that your organization should be backing up Explore the hardware, software, and services you can use to back up your systems Understand what's wrong with your current data protection system Pair your backed-up workloads to the appropriate backup system Learn the adjustments that will make your backups better, without wasting money

Download or read book Modern Data Warehousing Mining and Visualization written by George M. Marakas and published by . This book was released on 2003 with total page 300 pages. Available in PDF, EPUB and Kindle. Book excerpt: For undergraduate/graduate-level Data Mining or Data Warehousing courses in Information Systems or Operations Management Departments electives. Taking a multidisciplinary user/manager approach, this text looks at data warehousing technologies necessary to support the business processes of the twenty-first century. Using a balanced professional and conversational approach, it explores the basic concepts of data mining, warehousing, and visualization with an emphasis on both technical and managerial issues and the implication of these modern emerging technologies on those issues. Data mining and visualization exercises using an included fully-enabled, but time-limited version of Megaputer's PolyAnalyst and TextAnalyst data mining and visualization software give students hands-on experience with real-world applications.

Download or read book The Modern Data Warehouse in Azure written by Matt How and published by Apress. This book was released on 2020-06-15 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build a modern data warehouse on Microsoft's Azure Platform that is flexible, adaptable, and fast—fast to snap together, reconfigure, and fast at delivering results to drive good decision making in your business. Gone are the days when data warehousing projects were lumbering dinosaur-style projects that took forever, drained budgets, and produced business intelligence (BI) just in time to tell you what to do 10 years ago. This book will show you how to assemble a data warehouse solution like a jigsaw puzzle by connecting specific Azure technologies that address your own needs and bring value to your business. You will see how to implement a range of architectural patterns using batches, events, and streams for both data lake technology and SQL databases. You will discover how to manage metadata and automation to accelerate the development of your warehouse while establishing resilience at every level. And you will know how to feed downstream analytic solutions such as Power BI and Azure Analysis Services to empower data-driven decision making that drives your business forward toward a pattern of success. This book teaches you how to employ the Azure platform in a strategy to dramatically improve implementation speed and flexibility of data warehousing systems. You will know how to make correct decisions in design, architecture, and infrastructure such as choosing which type of SQL engine (from at least three options) best meets the needs of your organization. You also will learn about ETL/ELT structure and the vast number of accelerators and patterns that can be used to aid implementation and ensure resilience. Data warehouse developers and architects will find this book a tremendous resource for moving their skills into the future through cloud-based implementations. What You Will LearnChoose the appropriate Azure SQL engine for implementing a given data warehouse Develop smart, reusable ETL/ELT processes that are resilient and easily maintained Automate mundane development tasks through tools such as PowerShell Ensure consistency of data by creating and enforcing data contracts Explore streaming and event-driven architectures for data ingestionCreate advanced staging layers using Azure Data Lake Gen 2 to feed your data warehouse Who This Book Is For Data warehouse or ETL/ELT developers who wish to implement a data warehouse project in the Azure cloud, and developers currently working in on-premise environments who want to move to the cloud, and for developers with Azure experience looking to tighten up their implementation and consolidate their knowledge

Download or read book Data Pipelines Pocket Reference written by James Densmore and published by O'Reilly Media. This book was released on 2021-02-10 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting

Download or read book Modern Data Architectures with Python written by Brian Lipp and published by Packt Publishing Ltd. This book was released on 2023-09-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and Kafka Key Features Develop modern data skills used in emerging technologies Learn pragmatic design methodologies such as Data Mesh and data lakehouses Gain a deeper understanding of data governance Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionModern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You’ll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake. Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You’ll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you’ll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you’ll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you’ll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you’ll get hands-on experience with Apache Spark, one of the key data technologies in today’s market. By the end of this book, you’ll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What you will learn Understand data patterns including delta architecture Discover how to increase performance with Spark internals Find out how to design critical data diagrams Explore MLOps with tools such as AutoML and MLflow Get to grips with building data products in a data mesh Discover data governance and build confidence in your data Introduce data visualizations and dashboards into your data practice Who this book is forThis book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they’re not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.

Download or read book Cloud Scale Analytics with Azure Data Services written by Patrik Borosch and published by Packt Publishing Ltd. This book was released on 2021-07-23 with total page 520 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical guide to implementing a scalable and fast state-of-the-art analytical data estate Key FeaturesStore and analyze data with enterprise-grade security and auditingPerform batch, streaming, and interactive analytics to optimize your big data solutions with easeDevelop and run parallel data processing programs using real-world enterprise scenariosBook Description Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs. What you will learnImplement data governance with Azure servicesUse integrated monitoring in the Azure Portal and integrate Azure Data Lake Storage into the Azure MonitorExplore the serverless feature for ad-hoc data discovery, logical data warehousing, and data wranglingImplement networking with Synapse Analytics and Spark poolsCreate and run Spark jobs with Databricks clustersImplement streaming using Azure Functions, a serverless runtime environment on AzureExplore the predefined ML services in Azure and use them in your appWho this book is for This book is for data architects, ETL developers, or anyone who wants to get well-versed with Azure data services to implement an analytical data estate for their enterprise. The book will also appeal to data scientists and data analysts who want to explore all the capabilities of Azure data services, which can be used to store, process, and analyze any kind of data. A beginner-level understanding of data analysis and streaming will be required.

Download or read book Data Processing Handbook for Complex Biological Data Sources written by Gauri Misra and published by Academic Press. This book was released on 2019-03-23 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Processing Handbook for Complex Biological Data provides relevant and to the point content for those who need to understand the different types of biological data and the techniques to process and interpret them. The book includes feedback the editor received from students studying at both undergraduate and graduate levels, and from her peers. In order to succeed in data processing for biological data sources, it is necessary to master the type of data and general methods and tools for modern data processing. For instance, many labs follow the path of interdisciplinary studies and get their data validated by several methods. Researchers at those labs may not perform all the techniques themselves, but either in collaboration or through outsourcing, they make use of a range of them, because, in the absence of cross validation using different techniques, the chances for acceptance of an article for publication in high profile journals is weakened. Explains how to interpret enormous amounts of data generated using several experimental approaches in simple terms, thus relating biology and physics at the atomic level Presents sample data files and explains the usage of equations and web servers cited in research articles to extract useful information from their own biological data Discusses, in detail, raw data files, data processing strategies, and the web based sources relevant for data processing

Download or read book Modern Data Processing written by Robert R. Arnold and published by John Wiley & Sons. This book was released on 1978 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fundamentals of data processing; History of data processing; Data processing applications; Manual and mechanical data processing; Recording data for computer processing; Electronic data processing: introduction; EDP central processing unit; EDP auxiliary storage; EDP input-output devices.

Download or read book Python and R for the Modern Data Scientist written by Rick J. Scavetta and published by "O'Reilly Media, Inc.". This book was released on 2021-06-22 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together

Download or read book Modern Enterprise Data Pipelines written by Mike Bachman and published by . This book was released on 2021-06-25 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: A Dell Technologies perspective on today's data landscape and the key ingredients for planning a modern, distributed data pipeline for your multicloud data-driven enterprise