Sharma V., Balusamy B., Thomas J.J., Atlas L.G. (eds.) Data Fabric Architectures: Web-Driven Applications

De Gruyter, 2023. — 228 p. — ISBN: 978-3-11-100082-4.The immense increase in the size and type of real-time data generated across various edge computing platforms results in unstructured databases and data silos. This edited book gathers together an international set of researchers to investigate the possibilities offered by data-fabric solutions; the volume focuses in particular on data architectures and semantic changes in future data landscapes.There is a huge research gap between the traditional data fabrics, Virtualization tools, and the advanced and best tools in today’s date. Getting aroused with this fact this research is written to through light on the data fabrics and there are many data Virtualization tools available, and we’ve done the research to determine the best for small businesses. These tools should be versatile, easy-to-use and allow you to visualize data in a variety of ways to suit your business needs. The research clearly distinguishes the pros and cons of particular tools used in the research.Big Data platform components like Hadoop, data lakes, and NoSQL have made Big Data architectures more logical, enabling businesses to pursue insight-driven competitive advantage. Moving corporate data to these platforms, especially when dealing with distributed data across data centers, is hampered by security issues, complicated data structures, issues with moving historical data, big volumes, latency issues, and variable speed of ingestion. In contrast to a unified platform for insights, we discovered that the majority of enterprises are developing various repositories and platforms. One of the techniques that can assist in identifying probable in the social and medical sciences is a statistical tool. Using statistical techniques, it is possible to identify data creation as a mixture of data sets legitimate and false.Contributors include an international panel of leading researchers.
Describes Web-driven Data Fabric operations and solutions for Industry 4.
Includes data harvesting and visualization tools for Scalable Data Fabric in current market trends.Demystifying Industrial Trends in Data Fabric.
Web-Based Data Manipulation to Improve the Accessibility of Factory Data Using Big Data Analytics: An Industry 4.0 Approach.
The Overview of Data Virtualizations and Its Modern Tools in the Domain of Data Fabrics.
Data Fabric Technologies and Their Innovative Applications.
Enterprise Data.
Features, Key Challenges, and Applications of Open-Source Data Fabric Platforms.
An Open-Source Data Fabric Platform: Features, Architecture, Applications, and Key Challenges in Public Healthcare Systems.
Simulation Tools for Big Data Fabric.
Simulation Tools for Data Fabrication.
Security, Privacy, and Authentication Framework for Web-Driven Data Fabric.
Government Compliance Strategies for Web-Driven Data Fabric.