Data Fabric

Data Fabric is an architecture for building information work that provides the end user with a multitude of customization and administration options..

The main feature of the new DFs is the intensive use of neural network algorithms and tools that provide Big Data and AI (artificial intelligence) and Machine Learning (machine learning) functions to organize the most optimal data management schemes..

An architecture is usually understood as a closed (autonomous) ecosystem that provides an organization’s employees with access to corporate information, rather than a specific platform from a particular software vendor.

Data Fabric in modern companies: features and benefits

The data factory architecture emerged against the background of active application of interaction by large enterprises with large amounts of information under the standard constraints of management processes.

Modern Data Fabrics allow to efficiently cope with basic tasks in terms of storage and processing of disparate information. With Data Fabric, this information is easier to search, process, structure and integrate with other IT infrastructure systems.

Security issues are extremely acute in any corporate environment. In this regard, DF also stands out favorably against the background of alternative options, as it allows:

• provide reliable protection of information;
• realize information management with standard open APIs;
• maximize flexibility and fine-tune access to information for individual categories of network users.

The DF architecture aims for maximum transparency in the processes of analyzing, modernizing, integrating, and changing the data flow to meet the specific requirements of current business services.

Data Fabric – digitalization of DataOps processes

Data Fabric refers to the following mandatory set of characteristics and processes:

• The step-by-step processing of incoming data streams includes the mandatory participation of artificial intelligence. It helps to optimize processing algorithms, analyzes information faster, highlighting the most important aspects.
• Data sources, using the capabilities of modern graphical interfaces (APIs), get end-to-end integration (including Data Lake databases/stores).
• Microservice architectures are being used as a replacement for a single block of software platforms.
• The enterprise IT environment uses the largest number of possible cloud solutions.
• Information flows are orchestrated.
• Information quality improves after unification and virtualization.
• Whatever the type and volume of the data source, it can be accessed quickly (from databases, data warehouses, corporate data lakes, etc.).
• Secure and delimited access within the company (to different groups of users) for information processing. In parallel, there is a flexible setting of rights of each employee of the organization to information resources for each group of clients at the corporate level.

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The DF architecture is specially designed for the DataOps technology, with the help of which any changes in the data warehouse are fixed. As a result, the company gets an effective predictive layer for further development of business plans.

The use of artificial intelligence helps to optimize data storage and processing services, as well as to improve the quality of service of information resources and hardware.