Enterprises that want to embrace a data mesh framework do not need to scrape the current data architecture and develop a new one from scratch.
Businesses can design a customized approach to manage data to streamline the information flow. The data mesh architecture approach concentrates on developing trust in data and helps the decision makers to make the most out of the information gathered.
Organizations prefer data mesh over data lakes because of its capabilities to distribute the data and workflow assets into manageable and composable domains with inherent interdependencies.
However, integrating data mesh architecture into the enterprise tech stack has some inherent technical and operational challenges which it imposes on various businesses.
Here are some of the challenges of the data mesh framework that organizations need to be aware of before implementing it:
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Lack of testing automation investment
As data mesh architecture is a decentralized data ecosystem, enterprises need to ensure quality throughout the ecosystem. Enterprises are growing more complex with the advent of industry 4.0. Maintaining data quality throughout the channels and disbursed teams across different locations is crucial. Organizations can make every domain, channel, and the team responsible for the quality of data generated and stored with them. Designing and implementing the type of testing based on the nature of data collected through that channel will help to maintain quality. Businesses can bank on the fact that data mesh is read-only. It enables organizations to evaluate mock data and execute audits repeatedly against live data.
Data mesh architectures add a work burden on the domain teams
Data Mesh adds the responsibility for the data quality they generate and to create data products that are shared in the ecosystem. Developing and integrating a tightly coupled data pipeline is usually the turnaround for it. Since data changes at the application level add fragility to it, which might result in data silos which reduces the accuracy of data deposited in lakes, the reports generated by the data engineers will have multiple errors to it because of faulty data fed into the ecosystem. It will be a challenging task for the data ops team to resolve the errors.
Tightly coupled data products
Data mesh has elasticity in its nature because of the influence of micro services design. It has the capability to expand and contract to match with the topology as it scales in a few aspects and shrinks in a few. Enterprises can integrate various advanced technologies to scale as per the need to suffice the demands. Tight coupling is one of the most significant challenges of fully functional data mesh architecture. As micro services have an independently deployable principle, it gets implied in the data mesh framework as well. All the data sets in a mesh should be able to deploy at any given moment without making changes in the mesh. Data mesh to comply with this principle will consume a lot of time in versioning the scheme to apply with data products.
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Inability to automatically update data catalog
Data product discoverability is one of the key elements of data mesh architecture. Many data meshes integrate data catalogs or other additional mechanisms to enable their data products to be discoverable. Enterprises can use data catalog as product inventory in a mesh, usually using metadata to assist data discovery and governance. A discoverability approach is required to be updated to protect the usefulness of the data mesh architecture. Traditional documentation strategies are more hampering than no documentation. Enterprise can adopt documentation as a coding scheme to ensure updating the data catalog is a part of the review checklist for every query raised.