Data Mesh & Its Distributed Data Architecture
Going forward, data professionals have found a new way to address the scalability of sources through data mesh.
The enterprise vision to respond faster and deliver superlative customer experience requires an overarching remodeling of data management. So far, technologies have resolved the issues in storing & processing big data. It has also attained the competency of putting big data into deep analytics. While we are at it, the global market size for advanced data management solutions is expected to touch USD 122.9 billion by 2025.
However, the increasing diversity in type and number of data sources continues to obstruct seamless data lifecycle. Till now, data management landscapes were capturing & streaming data into a centralized data lake. The lake would further process and cleanse the sets in a fabric solution. Going forward, data professionals have found a new way to address the scalability of sources through data mesh.
A Data Mesh is a distributed architecture solution for the lifecycle management of analytical data. Based on decentralization, the Mesh eliminates the obstructions in data availability and accessibility. It empowers the users to capture and operationalize insights from multiple sources regardless of their location and type. Subsequently, it performs automated querying without having to transport it to a centralized data lake. The distributed architecture of a mesh decentralizes the ownership of every business domain. This means every domain has control over the quality, privacy, freshness, accuracy and compliance of data for analytical and operational use cases.
As the number of data sources continues to grow, data lakes are unable to perform on-demand integration. With data mesh, dumping large volumes of data into lakes is a practice on the verge of extinction.
The new data management framework ensures collaborative participation from all nodes, each controlling a specific business unit. It does so by following the principle of data-as-a-product. This means every data set is treated as a digital product that consists of clean, complete and conclusive data sets. These can be delivered to anyone and anywhere on-demand. For a rapidly growing data management ecosystem, Mesh is an instrumental approach for delivering organizational data insights.
The decentralization of ownership reduces the dependency on engineers and scientists. Every business unit controls its own domain-specific data. However, every domain still depends upon centrally standardized policies for data modeling, security protocols and governance compliance.
Any discussion around data management is incomplete and irrelevant if it misses out on the fabric architecture. There’s a myth around the fact that data fabrics and mesh compete with each other. That’s untrue. Gartner has discussed both titles side-by-side and cleared the air. A data fabric is a good old yet relevant architecture that drives continuous and optimal use of fabric in different industries. It automatically discovers and proposes a management architecture thereby streamlining the entire data lifecycle. It also assumes support for validating data objects and contextual references for reusing those objects. A Mesh does this differently by consuming current subject matter expertise and preparing solutions for data objects.
There’s a myth around the fact that data fabrics and mesh compete with each other. That’s untrue. In fact, fabrics could be instrumental in extracting optimal value from the Mesh architecture.
