The term data fabric is grabbing a lot of attention these days.  It has been identified as one of the Top 10 Data and Analytics Technology Trends for 2021. According to Mark Beyer, Distinguished VP Analyst at Gartner “ The emerging design concept called data fabrics can be a robust solution to ever-present data management challenges, such as the high-cost and low-value data integration cycles, frequent maintenance of earlier integrations, the rising demand for real-time and event-driven data sharing and more.” 

Do you know the exact meaning of data fabric? No? Then you are at the right place. In this blog, we will learn in and out of data fabric. What are you waiting for? Let’s go!  

What is Data Fabric?

As digital storage methods are becoming increasingly popular organizations are opting for data fabric. It is an integrated architecture that facilitates various data pipelines and cloud environments with the help of intelligent and automated systems. It is a new strategic approach to your enterprise storage operation that unlocks the best of cloud, core, and edge. It will help your organization unleash the power of data to meet business demands and gain a competitive edge. 

What are the uses of Data Fabric? 

According to Sean Knapp, founder, and CEO of Ascend,  “Data technology is the glue that binds an organization’s data systems together into a cohesive uniform layer.” It has many uses and here we have listed a few of them.

• It opens new paths to innovation, especially in accelerating the data and analytics lifecycle. 

• It can be used to conduct preventive maintenance analysis and reduce downtime.

• It can access insights from various data points and helps to predict the preventive maintenance cycle. 

• It shows how the customer uses their services, which eventually helps to enhance a customer's overall experience. 

• It improves data accessibility across healthcare organizations and academic institutions.

What are the Benefits of Data Fabric? 

Traditional data is no longer sufficient to meet business requirements such as universal transformation and real-time connectivity. 

• It automates data governance.

• Simplifies data integration.

• It provides a single environment for data and centralized data management and governance.

• It reduces data integration issues by eliminating the need to use multiple tools.

• It helps to accelerate the data transformation process by maximizing the value of data. 

• It increases Return on Investment. 

• It provides better accessibility to the real-time flow of information and data.

Also Read: Instructional Design and Technolgy 

What are the components of a Data Fabric? 

Data fabric is made up of different components that can be selected in various combinations. Therefore, their implementation of the data may differ. Let’s take a small walk looking at the main components of the data fabric. 

Augmented data catalog

It provides access to all types of metadata through a connected knowledge graph. It graphically displays existing metadata in an easy-to-understand form. 

Persistence Layer 

It stores data across a wide range of relational and non-relational models. 

Active Metadata 

It allows the data fabric to collect, exchange and analyze all kinds of metadata. The active metadata includes metadata that records the continuous usage of data by the system. 

Knowledge Graph 

A knowledge graph helps to understand the data fabric and make it searchable. 

Insights and Recommendations Engine 

It creates reliable and robust data pipelines for operational and analytical use cases. 

Data Preparation and Data Delivery Layer 

It retrieves the data from any source and delivers it to any target. By using methods like messaging, API ( Application programming interface), and virtualization. 

Orchestration and Data Ops 

Orchestration and data ops coordinate the work of all stages of the end-to-end workflow with data. It also determines when and how you should run pipelines and can control data generated by those pipelines.

Conclusion

With the rapid increase in technology and the problems around data multiplying, the use of data fabric can be seen as a potential solution toward a sustainable data future. It not only maximizes the value of data but also offers a better intrastate to manage it. 

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