A data fabric is an architecture that facilitates the end-to-end integration of disparate data pipelines and cloud environments using intelligent automated systems.

The data fabric architecture loosely couples data on multiple platforms with the applications that need it. An example of a data fabric architecture within a multi-cloud environment can be explained as follows; In this instance, one cloud like AWS manages data ingestion while another platform like Azure monitors data transformation and consumption. A third vendor, such as IBM Cloud Pak, provides analytics services. A data fabric architecture ties these environments together to create a unified view of the data.

However, each enterprise has different needs, and no single data fabric can meet the needs of all enterprises. With multiple cloud providers and a wide variety of data infrastructure implementations, businesses have plenty of options to choose from.

Use Case of Data Fabric

Over the past decade, various developments in hybrid cloud, artificial intelligence, Internet of Things (IoT), and edge computing have led to the exponential growth of big data, making data management more complex for enterprises. As a result, the integration and governance of the data environment have become an increasingly high priority as this growth of big data has created significant challenges such as data silos, security risks, and various bottlenecks that hinder decision-making.

Data management teams are tackling these challenges head-on with Data Fabric solutions. Leveraging data fabric solutions to unify disparate data systems, embed governance, strengthen security and privacy measures, and make data more accessible to workers, especially business users.

These data integration efforts through a data fabric enables more comprehensive, data-centric decision-making. For example, a company may have one platform for human resources data, another for supply chain data, and one more for customer data. This way, the data is stored in different, separate environments with the duplication of data. With the introduction of the Data Fabric, connections between data that didn’t exist before can be revealed, presenting data in a more cohesive manner to allow better decision-making. This also allows companies to have a deeper understanding of the customer lifecycle. And bridge the gaps in customer, product, and process understanding.

In short, a Data Fabric accelerates digital transformation and automation initiatives across the business.

Conclusion

Although the Data Fabric is still in its early stages from a deployment perspective, its data integration capabilities are useful for enterprise data discovery and are being adopted for a wide variety of use cases daily.

The use cases a Data Fabric can handle are not all that different from other data products, but it stands out for the scope and scale it can handle when it comes to breaking down data silos. By integrating across disparate data sources, businesses and their data scientists a data fabric can help create a holistic view of customers, which becomes especially useful for industries such as banking, insurance, and governance.

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Written by Hiran Wickramasinghe