Data Fabric (data factory) is an information management architecture that provides the end user with many configuration and administration options.

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

Architecture is usually understood as a closed (autonomous) ecosystem that provides employees of an organization with access to corporate information, rather than a specific platform from a specific software manufacturer.

Data Fabric in modern companies: features and advantages

The architecture of the data factory arose against the background of the active use of interaction between large enterprises with large amounts of information under standard constraints related to management processes.

Modern Data Fabric allows you to effectively cope with the main tasks in terms of storing and processing disparate information. With the help of Data Fabric, such information has become easier to search, process, structure and integrate with other IT infrastructure systems.

Issues related to information security are extremely acute in any corporate environment. In this regard, DF also stands out favorably against the background of alternative options, as it allows you to:

• to ensure reliable protection of information;
• implement information management using standard open API interfaces;
• maximum flexibility and fine-tuning of access to information for certain categories of network users.

The DF architecture is aimed at maximum transparency in the processes of analysis, modernization, integration, as well as changing the data flow to meet the specific requirements of current business services.

Data Fabric — digitalization of DataOps processes

A data factory means 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), receive end-to-end integration (including Data Lake databases/data warehouses).
• Microservice architectures are used as a replacement for a single block of software platforms.
• The corporate IT environment uses the largest number of possible cloud solutions.
• Information flows are orchestrated.
• The quality of information increases after unification and virtualization.
• Regardless of the type and volume of the data source, it is provided with quick access (from databases, data warehouses, corporate data lakes, etc.).
• Providing secure and delimited access within the company (to different groups of users) for information processing. In parallel, there is a flexible setting of the rights of each employee of the organization to information resources for each group of clients at the corporate level.

The DF architecture is specially designed for DataOps technology, which records any changes to the data warehouse. As a result, the company receives an effective level of forecasting for the further development of business plans.

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