Data Fabric abstract representation.

Data Fabric

and its importance

Data fabric is a modular and unified architecture for democratizing data access and delivering enriched data across the enterprise. This enterprise data fabric allows for common modeling, integration, orchestration and delivery of data to support all types of business needs.

The purpose is to create a unified view of associated data and its relationships. Enterprise data fabrics are becoming a primary tool to modernize your current data management approach and address existing data problems. Preparing for future data challenges requires a scalable architecture to deliver trusted data -- at the right time, in the right manner, and to the right data consumer across operational and analytical workloads. Data fabric is a recent innovation in enterprise data management and digital transformation. The most general application of data fabrics is to simplify access to data across a typical enterprise that is complicated by a wide variety of sources, schemas, formats and applications.

Business and technical benefits of data fabric

Data fabric serves a broad range of business, technical and organizational drivers.


  • Quicker time to gain insights and make decisions. Having ready connectivity and pipelines into the data lakes and warehouses.
  • A complete 360 view of any business entity across an enterprise. Provide a trusted view of a customer, claim, order, part, equipment etc.
  • Alert on operational risks and achieve micro-segmentation.


  • Breaking down data silos. Data tends to grow across an enterprise and integration problems grow exponentially due to different structures and formats.
  • Automation of data preparation, saving precious time for data scientists and data engineers from undertaking tedious repetitive data transformations.
  • An easy way to control and measure access to data. Use of standards like APIs and tags help manage data delivery to consumers.


  •  Provide a secure way to govern access and exposure of data. Meet various compliance needs.
  • A common taxonomy for sharing data across the enterprise. Self-service capabilities to improve efficiency and services.

Kobai's Data Fabric

 A new way of data management with the Kobai Platform

Kobai helps you build your Data Fabric using its self-service and collaborative knowledge graph platform. Create a contextual layer on top of your data lakes and warehouses that democratizes data and decision-making for all users within your enterprise.

Integrate and normalize data

Data exists in various forms and types. Data also sits in various source systems. Thus, there is a great need to integrate these disparate data and also normalize it for insights. The Kobai platform provides an easy interface to integrate and normalize data from RDBMS, local files, NoSQL, cloud storage, RESTful APIs, and time-series sources.

Contextualize data

Using the concepts of knowledge graphs and ontologies, the Kobai platform provides a codeless and self-service interface to capture business context and uses advanced object oriented concepts and inheritance. It provides a quick and simple to use method for subject matter experts and business analysts to link business functions and concepts across the enterprise.

Speedy insights

Kobai’s data fabric allows you to deliver business insights in minutes or hours compared to traditional BI approaches which can take weeks or months. Our interactive user interface makes the creation of ad-hoc queries quick and easy, accelerating the time it takes business users to gain insights and make decisions.

Secure delivery

Kobai allows for classification of entities within the knowledge graph and ontologies. It allows the ability to protect access to data and manage your privacy needs. You can also link it with your data governance policies to meet your compliance needs.

At a cloud scale

Kobai gets deployed in your private/public cloud environments. This allows for both horizontal and vertical scaling of the platform as your data needs grow.

Unlock your data today

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