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Data Fabric Architecture: Modern Way of Data Management and Integration

Data Fabric provides a single environment to access data and enable simpler and unified data management by connecting data from disparate sources. Such a data architecture helps businesses to discover unique, business-relevant relationships between the available data points.

Data fabrication is an architecture that is developed to standardize data management practices across multiple environments cloud, on-premises, and edge devices. Data fabric is listed among Gartner’s top 10 Data and Analytics trends.  It also allows enterprises to cost-effectively leverage technologies such as artificial intelligence and IoT by reducing time for integration design by 30%, deployment by 30%, and maintenance by 70%. With the increasing scalability and complexity of data from disparate environments in enterprises, a data fabric architecture helps to take the data to the right place and at the right time to make business analysis easy. Thus, it helps to unleash the power of data to meet business demands. 

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How Does Fabric Solve the Challenges of Data Silos?

Realizing the need for digital transformation is just not enough. It’s time to move on; time to embrace change and optimize digital structure at all layers of the enterprise landscape. Of late, businesses have adopted data fabrics in their management practices and created new data layers for workloads. At this rate, the market value could touch USD 4546.9 Mn by 2026, thereby making the technology accessible to everyone. 

As we all know, fabrics address many challenges. They eliminate the manual dependencies and empower data scientists to focus on other core tasks. One of the major issues solved by fabric is the uncertain volume of incoming data from multiple sources in silos. 

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Data Fabric: What Is It and Why Do You Need It?

Insight-driven businesses have the edge over others; they grow at an average of more than 30% annually. Noting this pattern, modern enterprises are trying to become data-driven organizations and get more business value out of their data. But the rise of the cloud, the emergence of the Internet of Things (IoT), and other factors mean that data is not limited to on-premises environments. In addition, there are voluminous amounts of data, many data types, and multiple storage locations. As a consequence, managing data is getting more difficult than ever.

One of the ways organizations are addressing these data management challenges is by implementing a data fabric. Using a data fabric is a viable strategy to help companies overcome the barriers that previously made it hard to access data and process it in a distributed data environment. It empowers organizations to manage mounting amounts of data with more efficiency. Data fabric is one of the more recent additions to the lexicon of data analytics. 

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Data Fabric vs. Data Lake: Operational Comparison

This article will focus on which is the most appropriate big data store for high-scale, real-time, operational use cases – data fabric vs data lake. It will also discuss data warehouses, as well as relational, and non-relational, databases.

What Are Operational Use Cases?

Data-intensive enterprises are driven by a broad array of real-time use cases requiring a high-scale, high-speed data architecture that can support millions of concurrent transactions. Examples include:

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Data Fabrics Modernize Existing Data Management

Introduction

Data management agility takes precedence among organizations with diverse, distributed, and disruptive environment. It is one of the most crucial deciding factors in determining a company’s potential to transform data into opportunities. But managing data remains an uphill climb thanks to advancements in big data and the Internet of Things (IoT). 

Data management is susceptible to errors and delays that can impact operational efficiency and value generation. Problems aggravate when traditional data management practices are used—and the overall performance of a company hits the skids. 

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Data Fabrics and Knowledge Graphs — A Symbiotic Relationship

The data fabric notion is gaining credence throughout the analyst community, in much the same way knowledge graphs have done so for years. Both technologies link all relevant data for a specific business purpose, which is why the most successful companies in the world employ them.

Amazon’s knowledge graph retains metadata about its vast product array; Google’s captures data about an exhaustive list of web entities of interest. Lesser-known organizations regularly deploy these mechanisms for everything from comprehensive customer views to manufacturing processes.