The Data Fabric: An Innovative Data Management Solution

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Business leaders are facing market pressure to extract value from their data pipelines within the constraints of limited budgets, time, and skills. Data is so diverse and massive it poses constant management challenges to business operators. Moreover, as it steps out of firewalls, business data now raises fresh security concerns for all businesses. In such a state of affairs, here comes another innovation in the “digital ecosphere” — the data fabric — to handle the scale, diversity, and governance of modern business data. So, what is data fabric?

Data Fabric: An Introduction                          

As enterprise data continue to battle the risks associated with diverse data types, corrupt data, insufficient storage, compliance shortfalls, and cyber threats, what’s needed is a robust and efficient technology solution to store, integrate, and manage vast volumes of data.

A data fabric is a distributed Data Management platform, where the sole objective is to combine various types of data storage, access, preparation, analytics, and security tools in a fully compliant manner, so that Data Management tasks become easy and smooth. With the rising business need to analyze real-time and historical data collectively, a data fabric provides the ideal Data Management infrastructure for speedy decisions.

A MarketsandMarkets.com report indicates that the global data fabric market is expected to grow from 558.0 million USD to 2125.1 million USD between 2016 and 2022. This prediction is further echoed in a 2018 Market Watch press release, which states that the data fabric market will experience massive growth by 2022.

The Unrealized Potential of Data Fabric

The benefits of data fabric are many: large data storage for diverse types of data, easy integration, and centralized access to multi-sourced data, single view of data across an organization, and superior tools for risk management. And yet, data fabric adoption is moving at a surprisingly slow pace.

A data fabric offers a complete infrastructure to provide consistent, controlled, and secure access to data and data services across multiple endpoints in a typical hybrid cloud environment. A data fabric facilitates easy management services to enable fast delivery of digital services for competitive advantage.

As hybrid cloud environments continue to embrace newer customer channels and novel technology-driven opportunities, a data fabric is another innovation to further the objectives of hybrid cloud. A data fabric may provide relief to business operators grappling with complex challenges of binding advanced technology environments like cloud, on-premise, and edge computing through a scalable Data Management solution.

Weaving Your Own Big Data Fabric offers a detailed technical discussion of the different layers constituting the Big Data fabric. The article suggests the most important benefits of Big Data fabric are the ease of integrating multiple data assets with data sources for enterprise-wide view, the availability of real-time information up to the last minute, the need for fewer resources due to data virtualization, and the easy replacement of legacy systems with Big Data systems without disrupting business processes.

With sensor-driven manufacturing units and business processes fast replacing legacy operations, the explosive volumes of incoming data may become a hindrance to smooth decision-making within enterprises unless technologies like a data fabric are embraced and implemented.

The Role of Data Fabric in a Connected World

In a growing world of connected devices, a data fabric can play a large role in facilitating AI-drive technologies and make a big impact on businesses. Interconnected data needs to be reliable, and that reliability can be promised by the layered architecture of data fabric. The power of data fabric is shown in Anzo — a “virtual intelligence” network comprising humans, ML algorithms, and data.

In the self-driving vehicle industry, researchers and autonomous vehicle manufacturers are still grappling with the challenges of untested or partially tested road conditions, which leads safety advocacy groups to raise concerns. The biggest challenge currently facing the industry is the generation of huge volumes of training data during object-detection model testing.

The Cutting Edge: Data Fabric, Driverless, and Data Gravity observes that though self-driving vehicle research teams have put their focus on computing power, advancements in sensors, and object-detection algorithms, not much has been written about managing the petabytes of data created during the object-detection model testing phases. The article explains how data fabric can help to manage this massive amount of training data.

Big Data Fabric in Action: Data Fabric Environment

The power, intensity, and capability of Big Data in responding to market changes, or in predicting risks or cyber attacks, or in streamlining business processes requires the matching infrastructure of data fabric. The MapR Data Platform, with live, dead, and batch data, provides a setting for implementing data fabric. MapR is a solution vendor for Big Data Management, and offers the MapR data fabric, which provides a host of tools like R or Spark, and a consolidated data-analysis platform for quick and accurate business decisions.

Wherever very high volumes of data or diverse types must be integrated and analyzed together, a data fabric may help provide a method in the madness. A Horton Works author reports that a Big Data fabric encompassing several data lakes is finally offering hope to businesses that have been struggling with Big Data Management for years. When the primary operation data lake fails for some reason, a backup data lake can take over for recovery, replication, or substitute operational purposes.

Final Thoughts

Secrets to Utilizing a Data Fabric features John Morrell, Senior Director of Product Marketing at Datameer, on the methods for utilizing a data fabric. In this video blog series, Morrell hosts discussions on several important technology topics like machine learning, Internet of Things, cloud, and Big Data. It may settle any question over whether data fabric is a hope or hype.

Image used under license from Shutterstock.com

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