The goal of the data fabric
On a fundamental level the goal for data fabrics is simply to offer an improved method of handling the data of an enterprise. It accomplishes this by replacing copies using controlled access, as well as offering a way to separate data from the software that generate it. This technique restores control the data owner, while making it much easier for them to collaborate with other users.
7 essential elements of a data fabric solution
It is a relatively recent technology and there are a variety of solutions that are being sold under the term data fabric. However, only a few of options could consider to be authentic Data fabric technologies. These are the elements to look for when selecting a solution
1. A network-based system that has universal controls, not data copies
A data fabric has to be constructed as it is a network. This network-based design which forms the basis of the other services a data fabric could offer. In addition the data fabric must make use of this network structure in order to provide the same access control for your data.
Since these controls are built into the level of data the controls will be present regardless of where the data is. The design of the network and data-level permissions remove the requirement for copying data one app to app, and also perform integration projects.
2. The ability to create autonomous data
This data that is autonomous has many applications and is an extremely efficient method of create innovative solutions. Technology can use information already present on the fabric, meaning that your solution that you designed for X can be easily changed to suit Y, without the need to rebuild crucial components.
Data fabric is a viable alternative to the conventional (but extremely inefficient) buy/build/integrate model. The creation of solutions based on data fabric could reduce the time to build by half, simply because it eliminates the need to conduct point-to-point integration tasks, and it may also provide advantages from there.
3. The existence of plasticity
Plasticity is the capacity to change the shape and structure of existing data in a more efficient way.
For businesses, plasticity removes obstacles that hinder the evolution of schemas. Builders can design integrations through Data contracts (i.e. models) to stop integrations from breaking when the schema of the data fabric evolves in time. This lets you change your schema for data without affecting dependences on external sources or within the internal structure. This includes relationships with and to various tables or APIs or queries.
4. Meaningful data ownership
Data ownership that is meaningful is crucial in securing privacy for individuals as well as security of the enterprise. It could be considered a crucial first step towards a super-intensive the future for AI/ML IoT as well as other technological advancements.
All attempts to regulate data, such as the GDPR and similar legislation will be a moot issue until the practice of the data copying process has been regulated and data ownership is given a proper significance. It is just as safe as the most vulnerable copy. Attempting to control data without taking action on the copies of data is similar to trying to limit the value of money without doing anything to stop counterfeiting.
5. Active metadata
Metadata are data that pertains to the data. It’s the primary factor in unlocking much of the potential of data fabrics. Metadata that is static which severely limits its use. Data fabrics make the metadata active, which means that it’s continuously updated and can be used to query or analysed, as well as handled just as traditional data. It is here that the value of a data-based fabric originates.
6. Metadata-driven experiences
A real data fabric must be able to replace conventional apps with experiences that are powered by metadata. The end-user’s experiences will not differ from apps or APIs however, making them is as easy as creating information in an Excel spreadsheet. These experiences that are based on metadata promise to change the way that solutions are developed in the near future, giving more control to users of the data, and giving business users the ability to build custom data solutions, without the need to involve IT resources.
7. The ability to create network effects
One of the greatest advantages of a truly data fabric is the potential to create network effects. This is the phenomenon that occurs when networks become more efficient and productive when the number of nodes connected. The first phone, for instance, was hardly worth the effort until the development of the second one and it only got better as more more phones were linked together.
Data fabric can provide this outcome for data from enterprises; the more data is available on the fabric the easier it will be to use it in new ways. This is a 180 degree change from the present model of point-topoint integration, which makes projects more complex and costly in the course of time.
What are the benefits of using data fabric software?
Data fabric prevents duplicate data, creating the basis for a meaningful data ownership. This enables future-proof solutions to meet the requirements of the new laws on data privacy that are being frequently introduced.
Data fabric brings the compounding effect of network effects on data. The more you use it, the better efficient and efficient it will become. This can provide a significant advantages to the early adopters of the data fabric.
The data fabric technique is frequently associated with data virtualization both of which offer new ways to handle enterprise data. However, there is a major difference between them the two: data virtualization is a simulation of change and data fabric provides real-time changes in the structure and physical form of data. It’s the difference between wearing on VR glasses to take a virtual tour around the Grand Canyon, and actually going there.