What Is Data Fabric? Definition, Architecture, and Best Practices

Data Fabric

What is Data Fabric?

Organizations can manage their data with the use of data fabric, an end-to-end data integration and management solution made up of architecture, data management and integration software, and shared data. Any employee of a company can have real-time access to data and a unified, consistent user experience thanks to a data fabric.

Data fabric is created to assist organisations in managing their data regardless of the different types of apps, platforms, and places where the data is kept, enabling them to tackle complicated data problems and use cases. Data fabric makes it possible for distributed data environments to have frictionless access and data sharing.

Best Practices to Data Fabric

1. Make use of the DataOps process concept.

DataOps can be a key facilitator even though the concepts of dataOps and data fabric are distinct. According to a model of the DataOps process, data processes, tools, and the people applying the insights are all intimately related.

Users are in a position where they can continuously rely on data, use the tools at their disposal effectively, and apply insights to enhance operations. The architectural design of the data fabric and this model are complementary. To fully utilise it, users will require a DataOps process model and attitude.

2. Avert building more data lakes.

The common issue with building data fabrics is that they could end up being just another data lake. The result is not a true data fabric if the architectural components—data sources, analytics, BI approaches, data transit, and data consumption—are in place but the APIs and SDKs are lacking.

Rather of referring to a particular technology, the phrase “data fabric” describes an architectural plan. The components of this design are interoperable, and it is ready for integration. The connection layer, flawless data transmission, and automatic insight delivery to newly connected front-end interfaces must therefore take precedence in companies.

3. Establish a data market place for amateur developers.

This integrated architecture typically generates and transmits insights straight to business applications or generates fragmented data repositories for IT or your data team to examine. Another method to take advantage of its potential is to create a data marketplace that democratises access for citizen developers.

Business users with a rudimentary understanding of data analysis and years of business analysis experience can use data from this industry to build new models for upcoming use cases. Businesses may create BI that is use case-specific while also enabling citizen developers to use it in creative and adaptable ways.

4. Use open source software.

Open source can be a game-changer when fabricating data. The most suitable technologies for its architecture are open-source ones because it is designed to be extensible and integration-ready.

Open source components might also assist you reduce your reliance on a single vendor because they might require a sizable investment, and you would want to protect that investment even if you subsequently decided to switch suppliers. A decentralised streaming data processing pipeline integrating big data and blockchain is made possible by the just newly published Open Data Fabric project, so be sure to have a look at it.

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