Data fabric and data virtualization are two different approaches to managing data. Data fabric is a newer technology, while data virtualization has been around for many years. Both have their pros and cons, and the right approach for your organization depends on your specific needs.
Visualizing your data is crucial in business because it allows you to see trends and patterns that you may not have been able to see before. By seeing your data in a visual format, you can make better decisions about how to run your business. Additionally, visualizing your data can help you communicate with your team and stakeholders more effectively, as they will understand the data more easily. This guide will cover data fabric vs. data virtualization and their key differences.
What is data virtualization?
Data virtualization is integrating data from disparate data sources into a unified form that can be accessed by authorized users. This is done by using a data virtualization layer, a specialized software application between the data sources and the users.
The data virtualization layer acts as a middleman, translating the data from the various sources into a common format. This allows users to access the data from any source as if it was all stored in a single location.
What is data fabric?
A data fabric, in information technology, is a type of data storage infrastructure that allows users to access and manage data from any location. Data fabrics are designed to scale out, meaning they can accommodate more users and devices as the network grows. They also allow users to store data in different formats and locations.
Data fabrics can be used in public, private, or hybrid cloud environments. For example, in a public cloud environment, it might allow a business to store data in an off-site data center, reducing the amount of storage space needed in-house. In a private cloud environment, it might allow different departments within a company to store data in different locations, making it easier to share information between departments.
What is the difference between data virtualization and data fabric?
Data Fabric and data virtualization are both ways of managing data, but they are not the same. The primary difference between these tools is that data virtualization creates a virtual copy of data while data fabric manages data. Data fabric is an umbrella term that encapsulates all the different ways you can store and process data. Data virtualization is a specific type of data fabric that allows you to create a virtual data layer that abstracts away the underlying data sources and makes them appear as if they are all part of the same data store.
Data fabric is more flexible and scalable than virtualization because it includes all the different ways you can store and process data. Data virtualization is more limited because it only allows you to abstract away the underlying data sources and make them appear as if they are all part of the same data store. Data fabric is also more resilient than virtualization because it is not as dependent on the underlying data sources. If one of the underlying data sources goes down, the system will still function. Data virtualization is more dependent on the underlying data sources because if one of them goes down, the system will not function.
Which data management tool is right for your business?
Data fabric is a comprehensive solution for managing data across the enterprise. It provides a single view of all data, regardless of where it is stored, and makes it easy to move data around and share it with others. Data virtualization is a more limited solution focused on managing data in virtualized environments. It allows you to create a single view of data spread across multiple physical databases and makes it easy to query and analyze that data.
Data fabric is a better choice for organizations that need a comprehensive solution for managing data. It provides a single view of all data, regardless of where it is stored, and makes it easy to move data around and share it with others. Data virtualization is a better choice for organizations that need to manage data in virtualized environments.
