Data Fabric Use Cases

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data fabric use cases

Data is expanding in volume, variety, and sources; therefore, so is the business need for trustworthy, accurate, and timely data for on-demand “competitive intelligence.” Data fabric use cases offer a long-range technological solution for handling the myriad challenges that come with such a complex data ecosystem. This “converged platform,” designed with a unique architecture and a bundle of data services, is well equipped to cater to diverse Data Management needs of a complex data ecosystem.

According to Allied Market Research, the Data Fabric Market is expected to reach $4,546.9 Million by 2026. The report Data Fabric Market by Deployment, Type, Enterprise Size, and Industry Vertical: Global Opportunity Analysis and Industry Forecast, 2019-2026 confirms that the data fabric market is projected to grow at a CAGR of 23.8% between 2019 to 2026 – culminating at $4,546.9 million by 2026. During this projected period, the North American data fabric market is expected to “remain dominant,” as the cloud service providers market, the top-most adopter of data fabric solutions, will also rise significantly in the same period.

The Data Fabric: An Innovative Data Management Solution explains that to mitigate the “risks associated with diverse data types, corrupt data, insufficient storage, compliance shortfalls, and cyber threats,” a data fabric offers platform tools for risk assessment, large storage for multi-type data, single-point access to multi-source data, and single data view across the enterprise.

What Is a Data Fabric?

A data fabric, one of Gartner’s top 10 trends in data and analytics for 2023, has been defined as:

“A data management design pattern leveraging all types of metadata to observe, analyze and recommend data management solutions. It enables business users to consume data with confidence and facilitates less-skilled citizen developers to become more versatile in the integration and modeling process.”

In the digital age, multiple customer touch points require the smooth flow of information for real-time analytics and immediate decision-making. A technological framework such as a data fabric provides a seamless analytics process across various data pipelines and service platforms.

In an era when reliable storage facilities are critical for the success of enterprise Data Management, a data fabric’s “re-architected storage,” with ample security, scalability, replication options, and high-performance characteristics, appears to be a perfect fit for the cloud infrastructure-as-a-service (IaaS) platform. John Morrell, senior director of product marketing at Acceldata, highlights the important elements of an enterprise data fabric in his video series.

Big Data Fabric Use Cases for Advanced Analytics

In typical big data projects, the foremost challenge is the high volume and complexity of the data used for analysis. The agility and flexibility of a data fabric infrastructure enables quick access to the right data at the right time for enhanced analysis.

As recent big data use cases have confirmed beyond doubt, the big data fabric was a game-changer, as explained in Big Data Fabric: A Necessity for Any Successful Big Data Initiative. The big data fabric platform offers end-to-end security coupled with assisted data integration and self-serve analytics capabilities for the average business user. The article also discusses another related technology – data virtualization, which is invaluable for:

  • Accessing a wide variety of data
  • Conducting big data analysis without technical skills
  • Exploring different use cases

According to Forrester, big data fabric is: 

“A unified, trusted, and comprehensive view of business data produced by orchestrating data sources automatically, intelligently, and securely, then preparing and processing them in big data platforms such as Hadoop and Apache Spark, data lakes, in-memory, and NoSQL.”

Data Fabric Use Cases for Business Applications

Modern businesses are thriving on the edge, so they need to exploit technology-enabled solutions in real time for a range of use cases. Such use cases may be:

  • Conducting preventive maintenance analysis to avoid downtime
  • Following up on customer sentiments to predict churn
  • Monitoring the markets to detect fraud
  • Conducting advanced predictive and prescriptive analytics for optimizing products or processes

Although these use cases are quite common within a business of any size, the technological means and modes to provide solutions are not same across the business landscape. The businesses that consider themselves “data-driven” and have already deployed advanced data technology systems are likely to succeed faster than their competitors.

A data fabric can mean the difference between success and failure to such a business, as this unique Data Management ecosystem offers a host of benefits, for example, flexibility, scalability, security, real-time analysis, and advanced analytics capabilities – all in one place. This Cloudera blog post assures that big data fabric overcomes “the challenges of insufficient data availability, unreliability of data storage and security, siloed data, poor scalability, and reliance on underperforming legacy systems.”

Data Democratization and the Data Fabric talks about the “interoperability” of multi-source data in a data fabric, in one way, indicating the democratization of data. The author explains how this framework simplifies Data Management tasks across cloud and on-premise data sources.

The MapR Data Management platform, for example, binds together “real time, dead, and batch” data for collective analysis. The MapR data fabric enables the user to grant access to both existing applications or tools and new tools. This platform enables access to “data in all forms” across “all locations.” The basic objective of MapR data fabric is to break down data silos for just-in-time access to all types of data, as explained in The Modern Data Fabric What It Means to Your Business.

The Talend data fabric solution helps IT teams switch between projects without any learning curve. This platform combines data integration tools, cloud, Master Data Management (MDM), Data Quality (DQ), and data integration tools on a “single platform with a common development and management environment.” The ultimate goal is increased productivity.

Data Fabric Use Cases for Machine Learning

Machine learning (ML) models can be used efficiently in a data fabric environment because data preparation time is minimized while the usability of the prepared data increases across models and applications. When data is distributed across an enterprise – on the cloud, on-premise, and at the edge (IoT) – data fabric provisions “controlled access” to secure data, which facilitates enhanced ML processes. The learning capabilities of ML models are significantly enhanced when the right data is fed to them at the right time.

Generally, a number of models may be used for one use case. In a typical business analytics scenario, data fabric can effectively tackle the challenges of distributed data piles and time-consuming ML processes.

Another interesting data fabric use case is “data in motion,” which must be accessed and analyzed in rest mode. Successful Machine Learning with a Global Data Fabric points out that by managing, controlling, and distributing data to data scientists for advanced analytics, data fabric platforms help them concentrate on the data analysis phase instead of wasting time on data preparation.

According to KD Nuggets:

“Reproducibility is important for data science and of course machine learning, so we need an easy way to reuse harmonized structured and unstructured data by managing catalogs of data sets.”

The KDNuggets tutorial on data fabric for ML teaches how a graph database and a semantic data layer together “integrates and harmonizes” all data sources in a data fabric environment.

Data Fabric Use Cases for Data Discovery

Data discovery is a very significant layer of the business analytics process, as this layer controls access to right data.When businesses use both data virtualization and data fabric platforms together, they gain significant advantages in business analytics. The data discovery layer unfolds what data is available for use, which is akin to the “load” function of traditional ETL tools. What makes the data fabric framework so powerful is the final Data Management layer, which runs through all other layers and manages security, Data Governance, and MDM.

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