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Data Fabric

What is Data Fabric?

Data fabric is a data architecture approach that automates data management functionalities to offer a unified view of all databases and data assets. It simplifies data access by connecting organizational data and reducing complexities of data storage architecture across multiple locations and environments.

A data fabric can be used to retrieve data irrespective of where or how it is stored without actually moving it into a centralized store. The framework created by the data fabric refines the earlier used data management strategies to address the multifaceted and complex data problems faced by organizations today.

Why Use a Data Fabric?

Traditional data integration tools are proving to be insufficient to meet the current business demands for real-time data availability. Data fabric, on the other hand, ensures data access is simplified across all resources, which is made challenging by the complexity of managing data in modern, distributed IT environments. The streamlined access has made data fabric the primary tool for extracting insights at the enterprise level.

How Does a Data Fabric Work?

Data fabric makes the data easy to access as if it were existing in local files and applications. It does so by creating an integrated layer on top of the data and centralizing it all in a single location.

The unified layer created in this way is directly connected to each database or data store, making analysis and sharing of information easier. Data fabric seamlessly unifies all data and thus allows collection of distributed data located in multiple applications.

Risks with Data Fabric

Since data fabric lets users access data from virtually any storage unit, it increases the security threats as well. It is essential that the infrastructure for the transportation of data is secured by firewalls and proper protocols are followed to avoid breaches. The threat of attacks on organizations is massive so security at all points of the data cycle should be the priority.

Benefits of Using Data Fabric

For organizations with a vast and geographically diverse set up and complicated data issues, data fabric becomes the ideal choice. There are several benefits associated with its use:

  1. Data Consistency: Having several copies of data stored in different systems can lead to errors in analysis and inconsistent insights for concurrent users. Data fabric makes it easy to avoid these issues by preserving a single source of truth, providing coherence and consistency in enterprise-level data.
  2. Scalability: Data fabrics are made to grow with the growing data and adapt to changing business requirements. They can accommodate an organization’s increasing data volumes while offering an infrastructure that will not disintegrate under pressure.
  3. Automated Data Integration: Data fabric automates tasks associated with unifying the data stored in a variety of formats and in multiple on-premises and off-premises locations. The earlier process had been slow and incapable of delivering real-time access to valuable insights. With data fabric, data siloes are also brought under control.
  4. Business Agility: Organizations are better able to adjust to evolving technologies, additional data sources and shifting business needs when they have a data fabric in place. It is an end-to-end technology to support new use cases without causing disruptions.

Key Elements of a Data Fabric Architecture

  • Data Catalog – Through the knowledge graph, access to data sources of all kinds is possible. By deploying a unique semantic layer that is in line with the organizational jargon, data catalogs forms the base for data fabrics which makes data accessible.
  • Data Processing – Once the data is curated to meet the specific needs of a user, the data fabric employs a set of tools to process it and make it ready for further analytics.
  • Knowledge Graph – As an integral part of the data fabric, knowledge graph helps visualize the connected data sources with the use of uniform resource identifiers, database schemas, etc. Knowledge graphs make the architecture functional and easy to search through.

Data Fabric Implementation and Its Challenges

There is currently a lack of standalone data fabric tools for establishing this architecture at the enterprise level. To implement it, a mix of data management and custom-coded software is needed to complete the framework.

Here, it is important that the data is efficiently processed and analyzed across a variety of locations and infrastructures, while maintaining consistency and keeping track of the data.

Data Lake vs. Data Mesh vs. Data Fabric: What’s the difference?

Data lake, data mesh and data fabric are all data management techniques that might seem similar but have some critical differences to set them apart.

Data Lake – The primary goal of a data lake is to gather unstructured data and store it in a single location without any additional integration. It takes out all the data from different locations and places it into one storage unit (the lake), without editing or filtering. The transformation is undertaken later when needed for analytics, rendering the data in a data lake unusable for real-time access by transactional systems. The data lake reduces the need for separate data storage, but it also increases the expense of data management and takes more time to construct.

Data Mesh – In contrast to a data lake, data mesh creates a direct connection between the data sources which allows for real-time accessibility. The architecture unifies all the organization’s data systems by utilizing intricate API interfaces. This means that massive data engineering work may not be required yet handling the APIs necessitates additional software development.

Data Fabric – By building a virtualization layer on the foundation of the data sources, a data fabric effectively makes it possible to connect data to a single, central platform. The data can be accessed and managed without moving from its original location. This technique eliminates the need for complex APIs or coding which makes it easier for users to transform and analyze the data.

How Does Data Fabric Architecture Deliver Business Value?

Data can provide business value only when it is comprehensible and accessible to all users in an organization. When a data fabric is properly established, it helps guarantee that those values are available for everyone with the utmost efficiency. Data fabric has to offer business value in two key areas that are –

Enhanced Data Governance and Compliance: By providing an integrated way of managing and governing data, data fabric contributes to the maintenance of data security, integrity and compliance with standardized criteria. This ensures that data is used properly and business intelligence tools produce trustworthy insights.

Self-Service Data Access: Organizations can establish a unified view of their data that can be accessed from a single point by connecting data from multiple sources, like databases, applications and external datasets, by using the data fabric. A culture of self-service analytics is promoted via the ease with which users can explore and evaluate data.

Enterprise Data Fabric Use Cases

  1. Customer support – Every day, new sources of customer data are being added to the already vast database. Information is being generated from the CRM systems, social media and customer reviews and feedback. It would be virtually impossible to classify and analyze all these enormous and diverse datasets without data fabric to control the impending flood. It assists in identifying and defining the various types of data for comparison and thus helps setting the parameters for analytics.
  2. Fraud identification and risk management – Phishing campaigns and cyberattacks cost businesses billions of dollars annually. By using a data fabric, they can see threats and dangerous behavior throughout the entirety of their company’s landscape—both internal and external—before they become an issue. This entails analyzing data from a variety of platforms and sources that contain sensitive information. It would be hard for humans to find anomalies and irregularities in huge datasets, but data fabric systems can use AI and machine learning techniques to do just that.
  3. Sales forecasting – By enabling companies to instantly combine and analyze data from a broad spectrum of both internal and external sources, the corporate data fabric architecture optimizes sales forecasting. This contributes to the creation of an exhaustive overview of the company’s sales data, becoming useful for producing precise and credible forecasts.
  4. Improved HR operations – Data from applicants, newly hired workers and current employees can be seamlessly integrated using a data fabric architecture. By offering a corporate-wide perspective on all aspects of HR operations, from time monitoring to employee well-being, it gives the company an advantage. Additionally, it provides the intelligence that HR teams need to identify and resolve minor issues before they turn into major ones.

Data Fabric Industry Examples

Many industries can be helped via the application of an innovative data management technology that is data fabric. Here are a few examples –

  1. Healthcare – Data is generated in multiple locations in the healthcare infrastructure, from the intake of the patient to their release. Many systems are involved in the process and thus silos are created which make it difficult to make sense of the different data that exists for every patient. Data fabric makes it easier to gain access to data located in multiple locations and systems, creating a centralized and complete view for all patients’ data.
  2. Manufacturing – The number of processes associated with the completion of the manufacturing cycle are numerous and intricate. Thus, the data that is generated is humongous and gives users the opportunity to explore it for extracting valuable insights. The data collected and stored by loT devices is located in disparate sources and data fabric allows integration and access without needing to move the data.
  3. Retail – The growing popularity of online shopping has necessitated retailers to provide its customers with omnichannel shopping experience. Personalizing the shopping experience is a major step forward for retailers and throughout the process new data is generated and stored. Data fabric integrates all the different data sources to allow more accurate predictions within the supply chain and consumer behavior.

Data Fabric and its future with Kyvos

Data fabric is a comprehensive data architecture that ensures accessibility and scalability by allowing enterprises to manage and connect their data across a variety of contexts with ease. Data Fabric is set for revolutionary expansion in the future, and Kyvos will play a significant role in determining this direction. Regardless of size or complexity, Kyvos uses its cutting-edge, massively scalable analytics platform to give businesses immediate access to big data insights. Kyvos’ seamless integration with Data Fabric accelerates analytics and improves data accessibility, allowing businesses to obtain actionable insights more quickly and effectively.

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