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Implementing Data Mesh : Design, Build, and Implement Data Contracts, Data Products and Data Mesh
As data continues to grow and become more complex, organizations seek innovative solutions to manage their data effectively.Data Mesh is one solution that provides a new approach to managing data in complex organizations.This practical guide offers step-by-step guidance on how to implement data mesh in your organization. In this book, Jean-Georges Perrin and Eric Broda focus on the key components of data mesh and provide practical advice supported by code.You'll explore a simple and intuitive process for identifying key data mesh components and data products, and learn about a consistent set of interfaces and access methods that make data products easy to consume.This approach ensures that your data products are easily accessible and the data mesh ecosystem is easy to navigate.With this book, you'll learn how to:Identify, define, and build data products that interoperate within an enterprise data mesh Build a data mesh fabric that binds data products together Build and deploy data products in a data mesh Establish the organizational structure to operate data products, data platforms, and data fabric Learn an innovative architecture that brings data products and data fabric together into the data mesh
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Data
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Data Mining for Social Network Data
Driven by counter-terrorism efforts, marketing analysis and an explosion in online social networking in recent years, data mining has moved to the forefront of information science.This proposed Special Issue on Data Mining for Social Network Data will present a broad range of recent studies in social networking analysis.It will focus on emerging trends and needs in discovery and analysis of communities, solitary and social activities, activities in open for a and commercial sites as well.It will also look at network modeling, infrastructure construction, dynamic growth and evolution pattern discovery using machine learning approaches and multi-agent based simulations. Editors are three rising stars in world of data mining, knowledge discovery, social network analysis, and information infrastructures, and are anchored by Springer author/editor Hsinchun Chen (Terrorism Informatics; Medical Informatics; Digital Government), who is one of the most prominent intelligence analysis and data mining experts in the world.
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2x RJ45 Economiser - Data/Data (STP)
Our RJ-MOD is an economiser cable adaptor that carries two connections over a single port and outputs the same two connections to different locations. This is the perfect solution to adding extra ports to your wall sockets. The RJ-MOD only works in pairs and only if they are connected to the same port number. The RJ-MOD is designed for use on data networks only. Specification: Allows two CAT5e connections to share one CAT5e port. 21cm lead. unshielded. For data networks.
Price: 16.13 € | Shipping*: Check Site €
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What are generic and dynamic data structures?
Generic data structures are data structures that can hold any type of data, such as integers, strings, or custom objects. They are designed to be flexible and reusable across different data types. Dynamic data structures are data structures that can change in size during program execution, such as dynamic arrays, linked lists, and trees. They allow for efficient memory usage and can adapt to the changing needs of the program. Both generic and dynamic data structures are important in programming as they provide flexibility and efficiency in managing and manipulating data.
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Why do Germans prefer creamy and mild over spicy and flavorful?
Germans tend to prefer creamy and mild flavors over spicy and flavorful ones due to their traditional culinary preferences. German cuisine often features dishes that are hearty and comforting, with a focus on simple and wholesome ingredients. Creamy and mild flavors are seen as more comforting and familiar, appealing to a wider range of palates. Additionally, the German palate tends to prioritize balance and subtlety in flavors, rather than bold and intense tastes.
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Why do Germans prefer creamy and mild instead of spicy and flavorful?
Germans tend to prefer creamy and mild flavors over spicy and flavorful ones due to their traditional culinary preferences. Creamy and mild dishes are often associated with comfort and familiarity in German cuisine. Additionally, the German palate tends to favor subtle and balanced flavors rather than bold and intense ones. This preference for creamy and mild dishes may also be influenced by the availability of ingredients and the influence of neighboring European cuisines.
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What is the formula for a dynamic data range in Excel?
To create a dynamic data range in Excel, you can use a formula that utilizes the OFFSET function. The formula typically looks like this: =OFFSET(starting cell, 0, 0, COUNTA(column range), number of columns). This formula allows the range to automatically adjust as new data is added or removed from the specified column range. By using the OFFSET function in this way, you can ensure that your data range always includes the most up-to-date information.
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Data Analyst : Careers in data analysis
Data is constantly increasing and data analysts are in higher demand than ever.This book is an essential guide to the role of data analyst.Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to.Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.
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Research Data Management and Data Literacies
Research Data Management and Data Literacies help researchers familiarize themselves with RDM, and with the services increasingly offered by libraries.This new volume looks at data-intensive science, or ‘Science 2.0’ as it is sometimes termed in commentary, from a number of perspectives, including the tasks academic libraries need to fulfil, new services that will come online in the near future, data literacy and its relation to other literacies, research support and the need to connect researchers across the academy, and other key issues, such as ‘data deluge,’ the importance of citations, metadata and data repositories. This book presents a solid resource that contextualizes RDM, including good theory and practice for researchers and professionals who find themselves tasked with managing research data.
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Big Data, Little Data, No Data : Scholarship in the Networked World
An examination of the uses of data within a changing knowledge infrastructure, offering analysis and case studies from the sciences, social sciences, and humanities. "Big Data" is on the covers of Science, Nature, the Economist, and Wired magazines, on the front pages of the Wall Street Journal and the New York Times.But despite the media hyperbole, as Christine Borgman points out in this examination of data and scholarly research, having the right data is usually better than having more data; little data can be just as valuable as big data.In many cases, there are no data-because relevant data don't exist, cannot be found, or are not available.Moreover, data sharing is difficult, incentives to do so are minimal, and data practices vary widely across disciplines. Borgman, an often-cited authority on scholarly communication, argues that data have no value or meaning in isolation; they exist within a knowledge infrastructure-an ecology of people, practices, technologies, institutions, material objects, and relationships.After laying out the premises of her investigation-six "provocations" meant to inspire discussion about the uses of data in scholarship-Borgman offers case studies of data practices in the sciences, the social sciences, and the humanities, and then considers the implications of her findings for scholarly practice and research policy.To manage and exploit data over the long term, Borgman argues, requires massive investment in knowledge infrastructures; at stake is the future of scholarship.
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Data Science : What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data - That You Don't
Did you know that the value of data usage has increased job opportunities, but that there are few specialists?These days, everyone is aware of the role that data can play, whether it is an election, business or education. But how can you start working in a wide interdisciplinary field that is occupied with so much hype?This book, Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data – That You Don't, presents you with a step-by-step approach to Data Science as well as secrets only known by the best Data Scientists. It combines analytical engineering, Machine Learning, Big Data, Data Mining, and Statistics in an easy to read and digest method.Data gathered from scientific measurements, customers, IoT sensors, and so on is very important only when one can draw meaning from it. Data Scientists are professionals that help disclose interesting and rewarding challenges of exploring, observing, analyzing, and interpreting data. To do that, they apply special techniques that help them discover the meaning of data. Becoming the best Data Scientist is more than just mastering analytic tools and techniques. The real deal lies in the way you apply your creative ability like expert Data Scientists. This book will help you discover that and get you there.The goal with Data Science: What the Best Data Scientists Know About Data Analytics, Data Mining, Statistics, Machine Learning, and Big Data – That You Don't is to help you expand your skills from being a basic Data Scientist to becoming an expert Data Scientist ready to solve real-world data centric issues. At the end of this book, you will learn how to combine Machine Learning, Data Mining, analytics, and programming, and extract real knowledge from data. As you read, you will discover important statistical techniques and algorithms that are helpful in learning Data Science. When you have finished, you will have a strong foundation to help you explore many other fields related to Data Science.This book will discuss the following topics:What Data Science is,What it takes to become an expert in Data Science,Best Data Mining techniques to apply in data,Data visualization,Logistic regression,Data engineering,Machine Learning,Big Data Analytics,And much more!,Don’t waste any time. Grab your copy today and learn quick tips from the best Data scientists!
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Do active mobile data consume data volume?
Yes, active mobile data does consume data volume. When your mobile data is turned on and you are using apps, browsing the internet, streaming videos, or downloading files, data is being consumed from your data plan. It is important to monitor your data usage to avoid exceeding your data limit and potentially incurring extra charges from your mobile service provider.
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Do activated mobile data consume data volume?
Yes, activated mobile data does consume data volume. When mobile data is turned on, it allows your device to connect to the internet using your cellular network, and any data used during this connection will be deducted from your data plan. Activities such as browsing the web, streaming videos, or using apps that require an internet connection will all consume data volume when mobile data is activated. It's important to monitor your data usage to avoid exceeding your plan's limits and incurring additional charges.
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What are master data and transactional data?
Master data refers to the core data entities of an organization, such as customer, product, employee, and supplier information. This data is typically static and does not change frequently. Master data is used as a reference point for transactional data. On the other hand, transactional data refers to the detailed records of day-to-day business activities, such as sales orders, purchase orders, invoices, and payments. This data is dynamic and changes frequently as business transactions occur. Transactional data is used to track and record the specific activities and events within an organization.
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Is data volume the same as mobile data?
No, data volume and mobile data are not the same. Data volume refers to the amount of data being used or transferred, which can include various types of data such as text, images, videos, etc. On the other hand, mobile data specifically refers to the internet data that is used on a mobile device, typically through a cellular network. Mobile data is a subset of data volume, as data volume can also include data used on other devices or networks.
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