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Two emerging architectures are designed to make data management easier. OvalEdge CEO Sharad Varshney explains what you need to know.
Today’s enterprises are struggling to manage large amounts of data spread across multiple internal and external systems. New data architecture models such as the data fabric and the data mesh are attractive to those seeking to manage distributed data. Should you consider one of these approaches? We asked OvalEdge CEO Sharad Varshney to explain the potential benefits and obstacles.
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Data Fabric Use Cases Emerging for Modernization
Upside: What is driving the adoption of data mesh and data fabric for data management?
Sharad Varshney: Both the volume and importance of data are increasing at a startling rate. As a result, there is a growing demand for agility. However, legacy systems are incapable of supporting this demand.
Today, on-premises solutions are becoming increasingly obsolete, and at the very least, companies must incorporate cloud architecture. Yet, even a modern, centralized, cloud-based architecture is insufficient in this rapidly evolving industry. To achieve the agility and scalability required, businesses must embrace decentralization.
A data fabric provides the essential connections supported by trusted procedures that businesses require to ensure highly scalable and easily accessible data management tools. With a data mesh architecture, organizations can use a data fabric to address latency issues by supporting a decentralized approach.
What is the difference between a data fabric and a data mesh?
The easiest way to differentiate between a data fabric and a data mesh is this: A data fabric architecture is centered on integrating and connecting the technologies that support data management, and a data mesh architecture focuses on the people and procedures behind data management.
Both approaches streamline data management by connecting various systems and technologies in a distributed landscape. However, you could say that data fabric architecture effectively underpins a data mesh by providing the flexibility, agility, and connectivity required if domain owners are to support seamless, decentralized data access.
Why is a data lake not a data mesh by default?
A data mesh is a relatively new concept; the first data lakes were conceived over a decade ago. The two concepts are fundamentally different. A data lake can be described as a centralized storage facility where data is organized and secured from various sources. Conversely, a data mesh connects data lakes and other data sources through distributed architecture.
The keyword here is distributed. Although a data lake is a centralized solution, data mesh architecture relies on decentralization. By enabling various domain owners to take charge of the data in their care, you can ensure that the experts most familiar with the data govern it most effectively.
How can data virtualization enable organizations to implement a simple, functional data mesh architecture?
Ultimately, a data mesh architecture can be created using data virtualization. By enabling users to create virtual data models that are unified and simple, data virtualization provides a quick way for domains to onboard various data products. It allows you to get rid of specific technical requirements, such as classification of structure, and instead work from a simplified, combined view.
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