What are the benefits of data fabrics network-design for data integration?
The purpose of data fabric technology is to connect real-time data from any source within an organization, including apps, data stores, and operational systems into a single, manageable platform hosted either in the cloud, on premise, or within hybrid environments.
While many data fabrics achieve this objective via traditional approaches to data integration such as point-to-point APIs and ETL technology, the latest data fabric designs offer networked-based architectures. This data-as-a-network approach enables organizations to use0 data fabric software to build links between their datasets rather than transferring copies between their applications and data stores.
By eliminating copies as a method for data integration, a network-based design maintains a single set of organizational data and this simplifies the management of access controls, metadata, and data governance while the linking capability eliminates much of the cost and delay associated with API/ETL-based integrations.
What are the best data fabric software options?
How does data fabric software differ from data virtualization tools?
The biggest difference between data virtualization tools and data fabric software is that data virtualization software creates virtualized copies of data that users can access without seeing factors such as location or connections. In contrast, data fabric software allows users to connect original data sources so users can access data while also seeing the context surrounding the data. Leading data fabrics also allow for data creation by end users working directly on the data fabric itself. In this way, the data fabric provides a bridge between legacy apps and systems and net new data. By providing this connected persistence layer, this supports the creation of new solutions in the form of data blends, data models from both sources.
What businesses can benefit from data fabric software?
At one end of the spectrum, data fabric software is ideal for large complex businesses and public sector agencies that need to integrate thousands of fragmented data sources. These organizations will appreciate being able to easily connect their data sources in real-time for operational use as well as granting controlled access to operational data to their business users and trusted partners, rather than a virtualized copy (aka data democratization)
The primary use cases for Data Fabric technology include data mastering, automated data governance, data protection, data privacy, advanced and self-serve data analytics, digital twins, AI/ML modelling, and custom application builds.
At the other end of the spectrum, data fabric technology is being increasingly adopted by SMEs and startups seeking to simplify their data management infrastructure, eliminate data integration costs, and deploy new solutions at a much accelerated pace.