Data fabric for data challenges: How this ‘digital weaving’ can work for you

Data is growing at an unprecedented pace. Everyone in the IT industry has heard or come across the issues associated with growing data storage. Every company irrespective of its size and shape is worried about running out of storage and may face another storage-related issue at any time. These issues can be caused by a wide array of reasons, including growing data needs, configurational errors, increased usage, and cyberattacks. But most of us often overlook the fact that data storage issues are not just confined to data capacity. Instead, there are several other factors that cause these storage issues. This includes various vectors such as performance, data and network protocols, hardware used, geographical locations, type of data storage, and more. While there are various IT solutions and tools available in the market to deal with these storage-related problems, they are meant to typically focus on any one or two of the storage issues specifically. True, a cloud service that provides online data storage options can help organizations deal with upscaling data capacities, but it is useless in dealing with application performance, or any other network or data-related storage issues. Is there anything that can help the situation? Yes — a data fabric.

The concept of fabrics has been around since the grid computing concept surfaced in the early 1990s. Fabrics are interconnected structures where all the different nodes in computing appear as a single logical unit. A data fabric is a derivative of this and is meant for data. (NetApp, which provides data fabric solutions to companies, has a good primer on what this technology is here.)

A data fabric is a common solution to all these data-related issues and is an emerging approach to deal with growing data and ever-changing IT needs in a better way. Different applications in an organization might be using different types of data and all of this data might be accessed in various ways. A data fabric can provide an overall solution in data management, performance, placement, access management, and data extraction-related aspects to the requesting users or applications.

data fabric
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Why organizations should care about data fabric

A data fabric serves as a single solution to most of the data-related challenges faced by organizations. With the growing needs of users and applications, the IT systems in the organizations are turning more complex than ever. One application might be deployed on a cloud, whereas another application might be hosted on-premises because of compliance constraints. In these scenarios, organizations need to have two separate environments to work across and handle these complex and distinct systems.

Data fabric can provide organizations with an ability to support all the disparate environments while providing continuous support for the existing applications and services. Every application will have its own approach to storing, retrieving, and accessing the data. While some applications use flat files, some need relational databases or even Big Data repositories. This difference in the type of data storage can create data silos, doesn’t just confine the organization’s ability to control and access its data but can also result in various other issues in security and maintenance. Data fabric can aid organizations in dealing with this and in avoiding the creation of data silos.

Advantages of data fabric

  • Creates a unified data environment
  • Risk mitigation
  • Reduced costs
  • Avoids data silos
  • Improved service and reliability
  • Central data flow coordination
  • Supports multiple locations
  • Allows easy scalability
  • Data fabric for unification

 

With the growing needs and technology, data-related problems are also growing. Unifying all the data generated from various applications can be quite a problem. Different applications in an organization or an enterprise store the same data in different formats, in different databases, in different locations, and can use different means of accessing and storing the data. This results in data silos, which can hinder the company’s operations.

Another challenge the companies are facing these days is the need for computing power on the edge of the network rather than in the company’s datacenter. To deal with this, organizations need to efficiently process the data generated by users through multiple applications, which can be made possible using the data fabric.

Moving the entire organizational data into a data fabric can address all these issues while also providing an agile environment. The benefits of data fabrics include data unification, providing a global data environment for optimization of costs, performance, security, and standards.

Data fabric for Big Data

Big Data fabrics is an emerging architectural concept that is meant to ease out the existing issues with Big Data. Big Data technologies like Hadoop are meant to handle the exponentially increasing data and are meant to extract value out of the data. Big Data technology aids the companies in saving costs, understanding the market analysis, and more. However, there are a couple of barriers such as lack of cohesion and automation that are holding back these technologies from being their best.

Big Data fabrics bring a level of cohesion along with automation, which can aid organizations in getting the most valuable insights out of the data. Big Data fabrics are meant to enhance Big Data in terms of data access, security, extraction, transformation, integration, lineage, governance, and other challenges.

Big Data fabrics are still in their emerging phase and as of now, they represent a need rather than being a complete solution. It aims at providing a real-time seamless integration and data access across the multiple data silos and processing the data at the edge for IoT, edge computing, and other critical needs.

With data sources getting more and more diversified, integrating them all is a persistent problem that needs to be addressed now. A data fabric is a very efficient solution.

Featured image: Shutterstock

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