Binary Data Illustrated as Fabric

What is Data Fabric?

The concept of data fabric has been gaining much prominence amongst data and analytics leaders. Data explosion brought about by growth in technologies such as IoT, Artificial Intelligence, hybrid multi-cloud environments and Edge computing has also increased complexities in enterprises. Data fabric provides a modern approach to data management and integration to help enterprises tackle these emerging challenges. Gartner identifying data fabric as one of the Top Ten Data and Analytics Technology Trends of 2021 has furthered popularized the concept.

Defining Data Fabric

Data fabric is an evolving and comprehensive design concept but in simple terms it can be defined as a unified data architecture and its related data services that help organizations manage data more efficiently. With the rapid growth in big data, new challenges have emerged including a rise in data silos, decision-making bottlenecks, and security-risks requiring focus on unification and governance of data environments. Data fabric as an architecture seeks to address these pain points by using automated and intelligent systems for end-to-end integration of data pipelines and cloud environments. It helps unify disparate data systems, embedding governance in the system, increasing security and privacy of data as well as providing integrated and enriched data to workers and business users.      

In simpler terms data fabric helps in accessing, ingesting, integrating, and sharing data in a distributed data environment. It can connect disparate data sources using components or connectors, eliminating the need for coding. Data fabric encompasses multiple environments including hybrid multi-cloud and on-premises cloud which could serve as the data source or the data consumer. With the help of technologies such as machine learning, active metadata management and knowledge graphs data fabric can strengthen data preparation, built-in data quality and data governance capabilities. For data ingestion and integration between data sources and applications, data fabric provides necessary capabilities.

Benefits of Data Fabric

Data fabric can help tackle many data management challenges and significantly improve productivity.

  • Removes over-dependence on technical resources by democratizing data and extending access of data to business users
  • Frees up technical resources to let them perform tasks more suited to their skillset
  • Provides seamless access to data leading to faster business decision-making
  • Protects data with appropriate safeguards around critical data to prevent data breaches
  • Integrates cutting edge technologies such as machine learning, metadata management, and semantic knowledge graph to unify data access and various data types
  • Removes data silos and allows access and collection of all data within a single environment
  • Eliminates need of multiple tools and helps access credible and healthy data, simplifying and unifying data management
  • Scales data management infrastructure to handle increased data volumes, sources, and apps
  • Provides access to data from disparate sources including legacy systems, data lakes, data warehouses, and databases
  • Provides a unified view of data by stitching together different environments such as hybrid multi-cloud and on-premises cloud
  • Reduces dependency on legacy software solutions and infrastructure
  • Allows for seamless addition and adoption of new data points, data sources and technologies without adversely affecting existing data management infrastructure


Data fabric as a concept is still new but there has been a marked increase in its adoption by enterprises for the numerous benefits it promises. According to Gartner, data fabric reduces time for integration design by 30%, deployment by 30% and maintenance by 70%.

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