Top Challenges in Data Mesh and How a Data Product Platform Resolves Them

The desire for more and better data encourages enterprises to elevate their management landscape, and data mesh is an integral addition to the stack. To put it simply, a mesh decentralizes the architecture and puts the domains in charge of the data capturing and sharing for the entire enterprise. The domain-driven design fits aptly in the hugely anticipated web 3.0. Add to it that the step ahead from traditional fabric solutions aims to bring down management costs, ensure faster streams, and ultimately think of data as a product.

Mesh, being a relatively newer concept, attracts challenges for the data teams and expects a more holistic approach. While it made it to the list of hot trends in data and analytics last year, the ongoing 2022 and the coming year will only scale its adoption.

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. 

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: