Products related to Data:
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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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The Data Revolution : A Critical Analysis of Big Data, Open Data and Data Infrastructures
Our world is becoming ever more data-driven, transforming how business is conducted, governance enacted, and knowledge produced.Yet, the nature of data and the scope and implications of the changes taking place are not always clear. The Data Revolution is a must read for anyone interested in why data have become so important in the contemporary era. Thoroughly updated, including ten new chapters, the book provides an accessible and comprehensive: introduction to thinking conceptually about the nature of data and the field of critical data studiesoverview of big data, open data and data infrastructuresanalysis of the utility and value of big and open data for research, business, government and civil societyassessment of the concerns and risks in a data-driven world and how to prevent and mitigate them.
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Transport Barriers and Coherent Structures in Flow Data : Advective, Diffusive, Stochastic and Active Methods
Transport barriers are observed inhibitors of the spread of substances in flows.The collection of such barriers offers a powerful geometric template that frames the main pathways, or lack thereof, in any transport process.This book surveys effective and mathematically grounded methods for defining, locating and leveraging transport barriers in numerical simulations, laboratory experiments, technological processes and nature.It provides a unified treatment of material developed over the past two decades, focusing on the methods that have a solid foundation and broad applicability to data sets beyond simple model flows.The intended audience ranges from advanced undergraduates to researchers in the areas of turbulence, geophysical flows, aerodynamics, chemical engineering, environmental engineering, flow visualization, computational mathematics and dynamical systems.Detailed open-source implementations of the numerical methods are provided in an accompanying collection of Jupyter notebooks linked from the electronic version of the book.
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Data
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How does data transport via fiber optic work?
Fiber optic data transport works by using pulses of light to carry data through thin strands of glass or plastic fibers. The data is converted into light signals, which are then transmitted through the fiber optic cables. The light signals travel through the fibers by constantly reflecting off the walls of the cable, allowing the data to travel long distances at high speeds. At the receiving end, the light signals are converted back into data for processing. This method of data transport is highly efficient and allows for large amounts of data to be transmitted quickly and reliably.
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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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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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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.
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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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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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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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What do Data Science and Data Engineering mean?
Data Science involves extracting insights and knowledge from data using various techniques such as statistics, machine learning, and data visualization. Data Scientists are responsible for analyzing and interpreting complex data sets to inform business decisions and solve problems. Data Engineering, on the other hand, focuses on the design and construction of systems for collecting, storing, and processing data. Data Engineers build and maintain the infrastructure that enables data to be accessed and utilized by Data Scientists and other stakeholders. They are also responsible for ensuring the reliability, scalability, and efficiency of data pipelines and storage systems.
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What does Renovierung 2 mean?
Renovierung 2 means renovation 2 in English. It refers to the second phase or iteration of a renovation project. This could involve further improvements, updates, or changes to a space or property after an initial renovation has been completed. Renovierung 2 could also indicate a follow-up renovation to address any remaining issues or to enhance the property further.
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How do you compare experimental data with theoretical data?
Experimental data is collected through direct observation or measurement, while theoretical data is derived from mathematical models or theoretical predictions. To compare the two, one can analyze the agreement or disagreement between the experimental and theoretical results. This can involve statistical analysis, such as calculating the percent difference or correlation coefficient, as well as visual comparison through graphs or plots. Additionally, one can also consider the sources of error in the experimental data and assess the validity of the theoretical model in explaining the observed results.
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