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How Can Unifying Data Fabric and Data Management Help Enterprises?

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Read more about author Nahla Davies.

Today, one of the most cutting-edge technologies is data fabric. Data fabric and data management can significantly improve business functions by connecting siloed data and making it accessible across divisions and workgroups. 

Utilizing a specialized architecture, on-premises data can be shared across multi-cloud and deliver unique insights unavailable with other data management techniques. In this article, we’ll look at what a data fabric is, its underlying architecture, and how it can improve your data management.

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What Is a Data Fabric?

A data fabric is a software architecture that helps enable end-to-end integration of data pipelines and network environments by using intelligent and automated systems. The exponential growth of big data through the hybrid cloud, artificial intelligence, Internet of things (IoT), and edge computing has caused increasing complexity for companies and enterprises to manage. Thirty years ago, many businesses were in the infancy of transitioning to digital systems. Today, these emergent technologies are at the forefront of most businesses and are an essential component of a Web 3.0 data network.

To stay competitive, data management teams are utilizing data fabric solutions to unify data storage systems, strengthen their network security and privacy measures, and make data more accessible across diffused companies.

Importantly, data fabric and data management integration permits better data-centric decision-making. Data fabrics allow businesses to align platforms and eliminate data silos. HR data platforms, supply chain platforms, and customer platforms can all communicate and share data. This means eliminating confusion and making connections between data to enhance cohesion. These types of advances facilitate digital transformation and automation across businesses and enterprises. Data fabrics are increasingly becoming part of executing an effective data strategy.

The Evolution of Data Fabric

As early as 2007, the initial concept of a data fabric first appeared. Since then, various data storage industry solutions have emerged. In 2018, NetApp’s data fabric chief architect, Eiki Hrafnsson, outlined Data Fabric 1.0. In sum, a data fabric is the ability to move your data anywhere. NetApp’s tech demo, which debuted in 2015, demonstrated a rapid transfer of 10GB of encrypted data between Amazon Web Services (AWS) and Azure cloud using a drag-and-drop interface. Today, data fabrics are more than just drag-and-drop interfaces that allow you to quickly transfer data between multi-cloud solutions. Data fabrics are now all about simplifying integration and connections between data applications and analytics across clouds, networks, and on-premises devices to facilitate digital transformation. This means that data fabrics are now more about integrating multiple systems to enhance data usage.

Data Fabric Architectures

In order to understand how data fabrics can benefit your organization, it’s important to understand the underlying architecture powering them. Data fabric architectures loosely connect data platforms and their applications.

What Architecture Enables Data Fabrics?

Data governance can be a challenge in a multi-cloud world, but data fabrics ease this process. Typical architecture in a multi-cloud environment can include AWS and Azure. In this case, AWS is utilized for intaking data, and then Azure processes and transforms the data. 

Third, a data lake is utilized to store customer data and ordering information. Finally, a third environment or service can provide analytical insights. A data fabric then sews these different services and environments together to create a single unified understanding of the data. This type of architecture, which connects multiple services, can improve cash flows and deliver great results for stakeholders.

The Six Layers of Data Fabric Architectures

Typically, there are six fundamental components to a data fabric:

  1. Data Management Layer: This layer manages data governance and data security.
  2. Data Ingestion Layer: This layer connects data together and locates connections between structured and unstructured data.
  3. Data Processing Layer: This layer takes structured and unstructured data and refines it so that only relevant data ends up being used.
  4. Data Orchestration Layer: The data orchestration layer then transforms relevant data, integrates it, and makes it usable for different work organizations across the enterprise.
  5. Data Discovery Layer: This layer is used for providing insights into your data and discovering new ways to connect disparate data. This means enterprises can see new ways to connect supply chain data and their customer data, for example.
  6. Data Access Layer: The final layer is the data access layer. This layer establishes permissions for user access and teams. It can also be used for compliance. This layer acts as the surface of your data by providing dashboards and data visualization tools.

Data Fabric and Data Management

By utilizing this type of layered architecture, data fabrics are able to superpower your data management. There are some additional key concepts that data fabrics use to further your data management capabilities.

Data Virtualization and Data Fabrics

Data fabrics actually depend on data virtualization. Data virtualization is a technology that helps data fabrics function by removing the requirement to physically move data from physical on-premises data sources and cloud sources. 

Data virtualization integrates the required metadata and creates a virtual data layer. This virtual data layer means that you no longer have to engage in standard extract, transform, and loading processes. In the end, enterprises can access data without having to move it, which means reduced work times and accelerated workflow.

Data Fabric Use Cases

On a practical level, there are a number of ways that data fabrics can be used. Since data fabrics are still relatively new, their full capabilities aren’t fully understood. That said, data fabrics excel at aiding in data discovery. Additionally, data fabrics help break down data silos and connect your organization. They connect data sources and provide new insights.

Finally, data fabrics have already seen use in banking, analyzing customer profiles, and streamlining fraud detection. Data fabrics allow deep and wide access to data, so they are perfect for doing analyses on preventative maintenance, return-to-work risk modeling, and other enterprise-wide data initiatives.

Other Advantages of Data Fabrics

Besides the previous use cases for data fabrics, there are other benefits too. Vendor lock-in can occur when you rely on a single vendor for a software solution. Data fabrics allow you to seamlessly switch between cloud providers or on-premises services.

Second, data fabrics empower enterprises to take control of their data management. When properly utilized, data fabrics eliminate data silos and can guarantee cloud-based backups and recovery.

Finally, data fabrics give your data full-stack visibility. This means that data can be leveraged on both your front end and back end. 

Conclusion

Data fabrics enable your enterprise to take full advantage of all of your data. They enable insights and data sharing unavailable using traditional data management techniques. Finally, their unique architecture means that they can provide enhanced data management capabilities.

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