What is a data fabric?

A data fabric is a methodological approach that breaks down significant data sources and distributes data to users. It allows accessing, integrating, ingesting, and sharing data across the enterprise. This can be either on-premises or on the cloud. Overall, the data fabric allows for a unified view of information that can be accessed from anywhere, regardless of the user's or the data's location. Due to this, it has become a leading method in providing data to analysis tools such as business intelligence and advanced machine learning tools that help it curate data.

The following subtopics will cover how each part of the data fabric process occurs.

• Accessing data: Data is stored in multiple locations, such as in data lakehouses, data warehouses, data lakes, standard relational database systems, and applications that have unique and vital data stored. Data fabric allows us to access this data without moving or copying from one repository to another. It creates a virtual layer to access this data anywhere within the enterprise's system. In doing so, it prevents copying data which can take a long time and additional take computation and require high costs. Moreover, it allows us data integration so we can copy data if needed or perform operations on it using ETL (extract, transform, and load) tools.

• Managing lifecycle: This can be split into the following two parts.

  • Governance: This part of the lifecycle acts as a controlling actor. Its job is to allow access to data for those users who have permission to access it. This prevents data from getting into the wrong hands and, in turn, creates data security and privacy. It does this by looking at the metadata of the information stored. The metadata shows policies that dictate which users should be allowed to view which data, called role-based access control (RBAC). Moreover, from its metadata, we can find its rich lineage information, which tells us where the data came from and which transformations have been done so we can check the data quality.

  • Compliance: This part of the lifecycle deals with data compliance policies depending on which system the data fabric method is running on. An example is the General Data Protection Regulation (GDPR) policy which works on data protection and privacy.

• Exposing Data: After data has been queried from one or many locations, it must be utilized somewhere. The data fabric must support multiple vendors and be available to open-source technologies. This data can be available to business analysts, data scientists, and application developers using multiple tools like business intelligence and machine learning, where datasets can maximize output and create fair systems.

Data fabric methodology is used in the virtual and logical collection of data from across locations to create a dataset with complete information. This can be used for applications such as business enterprises that require a distributed setup of datasets and a centralized business management system. The process of this method would be to collect data through a virtual layer and filter and sort it as required. Then this data is made available to those who require and have permission. This data can then be transformed by querying to create a business model to help the enterprise better understand itself and reach its desired goal.

Another example can be a simple online shopping store that can use this methodology to create a custom recommendation application. The diagram below shows the steps it would take.

Firstly, data is stored in a distributed system for an online store. This data is accessed through a Master Data Management (MQM) toolset. In our example, let us take the data. Now, data is run through the management lifecycle, i.e., it checks its governance and compliance policies. For example, sensitive information like credit card numbers and addresses are masked, and security and protection guidelines are enforced. Afterward, the data is published to the enterprise, which can be used by actors such as business analysts, data scientists, and application developers. From here, a developer can use the data to create a custom application or module recommending new products to customers with a history of purchases.

Finally, the data fabric approach allows a modern data integration and management concept with a unified approach to accessing, managing, and moving data. Moreover, its capacity to interact with data over different locations without copying data allows us greater flexibility while working with datasets. Therefore, it promotes an efficient system with policy control that can help use real-time data and achieve better insights into our enterprise. It can be said that data fabric plays an integral role in sustaining the ever-rising demand of modern data-driven enterprises.