“As part of ongoing data modernization efforts, organizations are constantly collecting, analyzing, and processing ever-larger amounts and types of data, which needs to be processed in a timely and efficient way and made available to a wider range of business decision-makers and end users,” says Anil Nagaraj, a principal with PwC US specialising in data analytics. “This requires robust, efficient, and secure data architectures that are capable of democratizing access to data and helping lighten the workload of an organization’s data teams.”
Both data mesh and data fabric can help eliminate duplication of workloads and facilitate interoperability and data democratization, which makes data more discoverable and accessible to a broad range of users within an organization. These benefits are critical to data modernization efforts as organizations seek to become data-driven and to maximize the benefits of AI.
With data mesh, each domain owns and manages its own data pipelines, controlling areas such as accessibility and formatting. This approach facilitates self-service use of data across an organization while allowing each business unit to process, store, and control its data. At the same time, a common governance framework promotes stronger data security, compliance, and governance practices across domain teams. This ensures data meets both internal standards for interoperability and external data security regulations.
Data fabric, meanwhile, pulls together data from legacy systems to provide a unified and consistent view of an organization’s data. This creates fluidity between different datasets, facilitating data movement, transformation, and integration, and overall management and governance. Compared with data mesh, data fabric provides a simpler and more integrated way of managing, processing, and analzsing data. Data can be accessed and analyzed in real time, at any time and from any location, which makes processing and analysis more scalable.
In practice, data fabric can complement data mesh, as it stitches together various environments to provide a unified view of data and help make new connections between datasets.
