Understanding Data Integration: Logical Data Fabric vs. Data Mesh

Data integration plays a crucial role in businesses today, especially as more elements of business rely on data. In Australia and New Zealand, spending on data analytics solutions reached $5.5 billion in 2022 and is expected to have a compounded annual growth rate of 13.3%.

In the past, enterprises have gone through cycles of centralization, decentralization, and various data management strategies. Recently, there has been a shift towards cloud-based platforms like AWS, Azure, and GCP. However, the challenge remains to collect and access data from various sources in a single location.

Two approaches that can help solve these challenges are logical data fabric and data mesh designs. A logical data fabric integrates data from different locations and formats, allowing stakeholders to view and act on this data in real-time. It achieves this using data virtualization, creating a logical layer on top of the data sources.

Data fabric allows business users to layer their own semantics on top of the data without modifying the underlying sources. This gives data analysts and decision-makers the ability to build tailored virtual data stores without the need to move the data or risk corrupting it.

On the other hand, a data mesh is an organizing solution that structures data, access privileges, processes, and workflows within an enterprise. Data domains are assigned to departments or functions, and each domain packages their data as products to be used throughout the enterprise.

A logical data mesh connects the networks of relevant data available to users, analysts, developers, and applications. It allows for the creation of new semantic layers for data domains without modifying the source data. This approach grants access privileges and business metadata to business intelligence teams in a separate layer.

The choice between a logical data fabric and a data mesh depends on the operational workflow and the size of the organization. Logical data fabric is a powerful way to integrate data and make it easily accessible, while data mesh provides a structure for managing data domains within an enterprise.

Overall, understanding data integration and choosing the right approach can help organizations leverage data for revenue generation and value creation.

Sources: IDC’s Worldwide Big Data and Analytics Spending Guide