K
Kathleen Martin
Guest
Data fabric and data mesh both strive to bring organization to the data that is spread across the databases or data lakes. Data fabric is very technology-centric, and data mesh focuses on organizational changes.
Every data-first company strives to or is already in the process of adopting a self-service business intelligence model. A lot of these companies are still not in a position to make their data fully accessible across their platform and scale it across to all their users across different verticals. For these companies, data that sits in siloes in a data warehouse or a data lake with no to limited facilitating capabilities as and when the teams require. Here is where data technologies like data mesh and data fabric come into play.
When looked at superficially, both might look fundamentally similar. After all, meshes come into existence from fabrics. Considering their impact on any IT system, it might be worthwhile to learn the difference between these two offerings to identify the right product fit for your organization. In many cases, finding the best of both worlds, an entity-centric data fabric can incorporate the data product concepts of data mesh, and the decentralization of data engineering might just be what the organization needs.
Data fabric
Noel Yuhanna, a Forrester analyst, was one of the first people to bring a definition to data fabric. Data management tools have come a long way from databases to data warehouses, then data lakes, depending upon the complexity of business solutions. Data fabric can be considered the logical next step in the data management process.
Data fabric is a metadata-driven process at its core, where it aims to connect a wide array of data sources and tools in a united and self-service manner. As the sizes of the data stored in the organizations keep increasing, the number of silos that hold this data as well increases. The type of data also widely varies in the way that it could be transactional or operational data.
With data fabric deployed over these repositories, data lakes, or warehouses, it brings about clarity in terms of the centralization of data across the organization. It makes data provisioning easier for the consumers downstream, be it data engineers, QA management engineers, or analysts. It should be noted, though, that while the management of this data is centralized, the access locations remain the same.
Continue reading: https://www.rtinsights.com/data-fabric-vs-data-mesh-key-differences-and-similarities/
Every data-first company strives to or is already in the process of adopting a self-service business intelligence model. A lot of these companies are still not in a position to make their data fully accessible across their platform and scale it across to all their users across different verticals. For these companies, data that sits in siloes in a data warehouse or a data lake with no to limited facilitating capabilities as and when the teams require. Here is where data technologies like data mesh and data fabric come into play.
When looked at superficially, both might look fundamentally similar. After all, meshes come into existence from fabrics. Considering their impact on any IT system, it might be worthwhile to learn the difference between these two offerings to identify the right product fit for your organization. In many cases, finding the best of both worlds, an entity-centric data fabric can incorporate the data product concepts of data mesh, and the decentralization of data engineering might just be what the organization needs.
Data fabric
Noel Yuhanna, a Forrester analyst, was one of the first people to bring a definition to data fabric. Data management tools have come a long way from databases to data warehouses, then data lakes, depending upon the complexity of business solutions. Data fabric can be considered the logical next step in the data management process.
Data fabric is a metadata-driven process at its core, where it aims to connect a wide array of data sources and tools in a united and self-service manner. As the sizes of the data stored in the organizations keep increasing, the number of silos that hold this data as well increases. The type of data also widely varies in the way that it could be transactional or operational data.
With data fabric deployed over these repositories, data lakes, or warehouses, it brings about clarity in terms of the centralization of data across the organization. It makes data provisioning easier for the consumers downstream, be it data engineers, QA management engineers, or analysts. It should be noted, though, that while the management of this data is centralized, the access locations remain the same.
Continue reading: https://www.rtinsights.com/data-fabric-vs-data-mesh-key-differences-and-similarities/
