Data acquisition and virtualization

  • How data virtualization transforms data to information?

    While all data remains in the source systems, data virtualization creates a virtual/logical layer that delivers real-time data access with the possibility to manipulate and transform the data in virtual views.
    This virtual layer delivers a simpler and more time-efficient data management approach..

  • Is virtualization a data mining technique?

    It helps with data mining, it enables effective data analytics, and is critical for predictive analytics tools.
    Effective use of machine learning and artificial intelligence is unlikely without data virtualization.
    It should be noted that data virtualization is not a data store replicator..

  • What do you mean by data virtualization?

    Data virtualization is an approach to integrating data from multiple sources of different types into a holistic, logical view without moving it physically.
    In simple terms, data remains in original sources while users can access and analyze it virtually via special middleware..

  • What is the difference between data integration and virtualization?

    Managing and integrating multiple applications can be a challenge for IT teams.
    Data integration enables fast data extraction into a storage solution for a cohesive and unified view.
    Data virtualization can help make sense of the unified data by making it accessible directly within reporting tools for deeper analysis..

  • Data virtualization is a technology that enables organizations to access and query data from disparate sources in real-time, without the need for physically moving or replicating data.
    It serves as a middleware layer that provides a unified, virtual view of data, irrespective of its location or format.
  • Data virtualization is a virtualized architecture layer that “sits” on top of those data sources and connects them. (Note: This is distinct from “data visualization,” which refers to things like charts and graphs that help explain data.)
  • It helps with data mining, it enables effective data analytics, and is critical for predictive analytics tools.
    Effective use of machine learning and artificial intelligence is unlikely without data virtualization.
    It should be noted that data virtualization is not a data store replicator.
Data virtualization is a method of managing data that enables applications to retrieve and manipulate data without needing specific technical information about the data, such as how it was formatted at the source or where it is physically located.
Data virtualization software aggregates structured and unstructured data sources for virtual viewing through a dashboard or visualization tool. The software 

How do you implement a data virtualization architecture?

In order to successfully implement and manage a data virtualization architecture, IT teams need a number of elements in place, including:

  1. an abstraction tier
  2. a metadata management layer and proper governance processes

Here, experts detail the most important components necessary for data virtualization.
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What is data virtualization & why is it important?

Data virtualization is inherently aimed at producing quick and timely insights from multiple sources without having to embark on a major data project with extensive ETL and data storage.
However, data virtualization may be extended and adapted to serve data warehousing requirements also.

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What is data virtualization (DV)?

DV enables established technologies like cloud, big data, and advanced analytics platforms to work in tandem to produce superior Data Management solutions that traditional data warehouses failed to achieve.
Through data virtualization platforms, vendors are offering a one-stop solution for data collection, management, and data services delivery.

Data acquisition and virtualization
Data acquisition and virtualization

Virtualization module in the Linux kernel

Kernel-based Virtual Machine (KVM) is a free and open-source virtualization module in the Linux kernel that allows the kernel to function as a hypervisor.
It was merged into the mainline Linux kernel in version 2.6.20, which was released on February 5, 2007.
KVM requires a processor with hardware virtualization extensions, such as Intel VT or AMD-V.
KVM has also been ported to other operating systems such as FreeBSD and illumos in the form of loadable kernel modules.

Application virtualization software

Symantec Workspace Virtualization is an application virtualization solution for Microsoft Windows by Symantec, now known as external text>Symantec Endpoint Virtualization Suite (SEVS).

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