Enterprises were first introduced to the term “Data Fabric” in a 2016 report - “Big Data Fabric Drives Innovation and Growth” by an analyst from Forrester-Noel Yuhanna. Since then, the Data Architecture landscape has only further validated and acknowledged the role of Data Fabric in the success of an Enterprise. 

Data fabric is becoming popular as businesses look for means to make better use of their data assets. It is the modern data architecture approach to aggregate data from distributed sources. Enterprises are striving to integrate data from distributed sources to establish an integrated, modular, agile, and composable data management design. This is essentially a technology architecture pattern that allows maximizing different configurations of technology solutions. It also eliminates data integration issues, improves the quality of data, and simplifies data sharing and governance.

Why do you need Data Fabric? 

Modern Businesses are driven to make decisions based solely on Data. Data drives the competitive advantage.  It is therefore critical that these decisions are made based on real time validated set of data and is not compromised by data in silos. In any organization the data is distributed in multiple geographic locations, legacy and COTS applications, files etc. For any organization, data distribution is generally as follows:

• Multiple environments - On-premises, Private cloud, Public Cloud, Multi Cloud. 
• Data Lake, Data Warehouse, SaaS applications, COTS on-prem applications, legacy applications and so on. 
• Files with different formats and locations. 

Any data-centric organization requires a unified approach that allows data to be accessible to the right people at the right time. Data Fabric is an architecture that provides end-to-end data integration solutions that unifies disparate data systems to provide holistic, on demand access to Enterprise data with the right governance and security. It enables data democratization with automated data discovery as well as effective and intelligent data exploration techniques. In a nutshell, Data Fabric allows enterprise-wide visibility and makes data available across the organization.

Reasons for going the Data Fabric way:

• Establish standardization & consistency in sharing information at any point in time. 
• Forearm your Enterprise with Operational Intelligence, know how you can become more operationally efficient with your information.
• Utilize information which up until now was in silos, can now participate in information exchange across the organization seamlessly and build robust digital threads.
• Enable real-time data analytics and provide actionable insights.
• Facilitate high data quality, curation, ingestion, and orchestration of the data.
• Build knowledge graphs to connect data across several end points across structured, unstructured, or semi-structured data. 

There are innumerable applications once you adopt a data fabric approach. You can manage your supply chains optimally, forecast with more clarity, maintain regulatory compliances, govern your data efficiently and build an insightful Business Intelligence across the organization.

These usages exist with any data architecture styles such as data mesh, data lake, and this is indeed true. However, the interoperability of data between diverse sources in a seamless fashion using Data Fabric saves time and allows you to access relevant and interrelated data on demand. This interoperability of data between first class application systems and edge devices makes it possible to show a holistic view of the information which connects all the dots. This is what makes the Data Fabric approach one of a kind.

Furthermore, you can add AI & ML to your data fabric to manage complex workloads, support parallel data extraction, automate data discovery, and tackle data complexity efficiently.

Data fabric architecture approach offers several advantages over other data architectures. Here are some of the key reasons:

1) Agile Data Integration:
Data integration between different sources is made easy, quick, and efficient, without requiring a lot of manual effort.

2) Lower Data latency and real-time data access:
Data needs to be accessed in real-time time without having to move or replicate it across diverse systems, which in turn reduces data latency. This is possible using Data Fabric.

3) Lower Total Cost of Ownership:
The need to store data in Data warehouses is completely eliminated along with the cost of synchronization of data. This results in lower total cost of ownership.

4) Composable, Scalable and Flexible approach:
Large volumes of data can be handled rather easily when using a Data Fabric. It can accommodate changing technical configurations and this data architecture can incorporate new business demands as it evolves with agility.

5) Data Security and Governance
This centralized data management platform leverages existing security and governance without having to build a separate external security system around it. It simply piggybacks on inherent mixed security protocols and extends it throughout the organization.

eQube®-Data as a Service (DaaS) Platform establishes a Data Fabric with connected network of integrated data, applications and devices that puts the power of analytics in the hands of end users leading to Actionable Insight. Data from any source can be aggregated using eQube's Data Virtualization layer and exposed as a Web service, REST service, OData service or API.

To know about how the Low/No-Code eQube Platform helps to build Data Fabric visit our website to checkout our customer case studies and whitepaper. 

You can also reach out to us at info@1eq.com.