As organizations start to use more applications, the data they generate becomes increasingly complex, siloed, and often inaccessible.
Sharing data across large organizations creates its share of complexities. For example, some data might exist on a data center located on-site in a particular jurisdiction, while other data could be on a public cloud (AWS Azure, or Google Cloud).
A typical company stores data in multiple on-site locations and online in public or private cloud platforms.
Processing this data to generate insights may require accessing data stored in different formats across different file systems, databases platforms, and locations.
It is difficult for organizations to integrate and analyze their data quickly. This problem is exacerbated by the fact that the amount and forms of data organizations generate are increasing rapidly.
Data Fabric aims to solve this problem by combining several management technologies such as; Data integration, data orchestration, data pipelining, data catalog, and data governance.
A Data Fabric is a mix of architecture and technology that aims to ease the difficulty and complexity of managing several different data types.
It is deployed across a range of platforms, and it uses numerous database management systems. A data fabric provides a consistent and consolidated user experience and access to data for any member of an organization globally in real-time.
The goal of a Data Fabric is to help organizations understand and manage all their data regardless of:
It is an end-to-end solution that integrates and manages data from various sources and provides access to all the data on a platform that enables easy access in a distributed data environment.
A good Data Fabric solution must have the following features:
Future-proof infrastructure: This will reduce the disruptive impact of new data types and technologies. It will allow for new infrastructure deployments and integrations without impacting existing and legacy systems.
Visibility: Users should be able to measure data availability, reliability, and responsiveness.
Security & Governance: A clearly defined policy to secure and govern all the data.
Platform & Application Agnosticism: The platform should have the ability to integrate with all types of platforms and applications.
Data Virtualization: By virtualizing the data and creating a single representation of it from several sources, the need to move and copy the data will be reduced.
Unified Data Semantics: An analytics platform that allows the consumers of data to define business meaning and get a single source of truth regardless of the form and structure of the data.
Data Fabric helps users gain a real-time 360-degree view of any business entity. It also helps lower the cost of owning, operating, and scaling legacy systems. Lastly, it reduces the time required to generate business insights.
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