Data Fabric Architecture.

Data fabric architecture provides a centralized, connected layer of integrated data that democratizes cross-company data access.

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Advantages of a
data fabric architecture

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An always-fresh, connected data layer

Unify data from different sources and technologies, under a common semantic layer that's easily understood by business and IT.

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Easily accessible
trusted data

Enable authorized data consumers to instantly access trusted data through APIs, SQL, streaming, messaging, or virtualization.

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Reduced total
cost of ownership

Integrate, leverage, and extend your company's existing data management tools to implement data fabric architecture.

Data fabric architecture

Centralized data management

Data fabric architecture

A data fabric architecture is modular, and integrates with a company's existing data and analytics tools.

Although it is a centralized data management design, a data fabric may be deployed as a distributed network of data fabric nodes, to support high scale and high availability.

Data fabric design makes data integration and delivery possible through any combination of integration methods: bulk (ETL), APIs, data streaming, messaging, and data virtualization.

Data fabric architecture ingests data from modern cloud applications, as well as from on-premise legacy systems, regardless of the format and structure of the data. 

Data fabric fuels operational workloads that require real-time, trusted data - for example, a holistic customer view delivered to a CRM application. Data fabric should also enable data pipelining of integrated data into data lakes and warehouses, for analytical workloads.

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Data management automation

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Data Fabric automates data preparation, and orchestrates its delivery. In fact, research from analyst firm Gartner shows that  data fabric can cut the time to design, deploy, and support data pipelines, in half.

It becomes a trusted data management partner over time, by first observing, and then learning, data usage and performance behaviors. The long-term vision for data fabric is to provide data teams with automated recommendations for optimizing data delivery modes, data pipelines, deployment configurations, and more.

Key principles of
data fabric architecture

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Reusable data products and pipelines

Data products and pipelines are packaged and published, enabling quick discovery and reuse to support new workloads. They can easily be adapted, without ever impacting the consuming applications or shutting down the data fabric. 

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Use of knowledge graphs, semantics, and active metadata

Data and analytics teams have complete visibility into available data products and pipelines, to avoid creating overlapping data assets. They can combine data integration methods to best support the required use case.

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Support for operational and analytical use cases

Fabric fuels operational use cases by delivering real-time, unified data – from any sources into transactional systems. It also prepares and pipelines data into data lakes and data warehouses in a state that's ready for immediate analysis.

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Abstraction of infrastructure and storage-level details

With a data fabric, less technical users can quickly find, access, integrate, and share data – while involving business SMEs  in the data modeling process. The end result is reduced cycle times of accessing ready-to-use, trusted data.

 

Data Product Platform
deploys as a data fabric

K2View Data Product Platform is modular in its architecture, and provides integrated, end-to-end data fabric functionality – from data integration and preparation, to data orchestration and delivery.

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Data Integration
Ingest and unify data from multiple sources, then pipeline it to target systems, always ensuring data integrity.
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Data Virtualization
Provide a logical abstraction layer to underlying systems, to make it easy to access trusted data.
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Data Preparation
Make data lakes and data warehouses instantly and always ready for analytics.
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Data Orchestration
Control data movement and data transformation, from source to target systems, code-free.
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Data Catalog
Discover and visualize metadata structure and lineage, from source to consuming microservices.
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Data Governance
Control data synchronization, access, integrity, and security – using configurable rules and processes.
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Data Masking
Protect data at rest, in use, and in transit – across production, testing, and analytics environments.
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Microservice Automation
Generate, debug, and deploy web services in minutes, with an easy-to-use, no-code/low-code framework.
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Data fabric expectations

By 2024, Data Fabric deployments will quadruple data utilization outcomes by cutting human-driven data management tasks in half

Mark Beyer, Distinguished VP Analyst, Gartner

Implement a data fabric architecture
with a real-time data product approach

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The K2View Data Product Platform lets you to create and deliver data products, quickly and easily, to fuel operational and analytical workloads, and enable the most sophisticated data mesh, data fabric, and data hub architectures – on premises, in the cloud (iPaaS), or across hybrid environments.

 

Learn more

Learn more about data fabric