The following are characteristics of a data mesh:
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Data diversity: Treats data platforms as independent data domains, connecting data domains into the mesh to create business-oriented data products that can support strategic goals. The information persisted in their respective environments comes from different applications or source systems adding to the overall data diversity that analysts and data scientists benefit from.
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Data democritization: Rather than try to combine multiple domains into a centrally managed data lake, data is intentionally left distributed. By adopting this approach, your organization’s data becomes democratized and becomes assessible to more teams.
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Data governance: Improve data governance by pushing data access policy down into the data domains. Large enterprise organizations experience challenges when scaling their data governance to the number of subscribers because this is managed centrally. A data mesh allows for disparate teams to inherit the data governance policy from the data producer domain.
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Searchability: Establishing a central mechanism for data discovery is valuable for analysts and researchers to know what data is available. An enterprise-level data catalog contains the metadata of the organization’s data assets. The data catalog contains data attributes, data quality, data classification, and a business glossary of the data.
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Data sharing: Provide self-service data sharing features to allow domain owners to grant access to consumers.
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Increased flexibility: Increase data flexibility by implementing an enterprise data mesh. A data mesh provides organizations greater agility as data becomes widely available and supports faster data-driven business decisions.
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Reusability: A data mesh increases the adoption of reusable data pipeline design patterns to share data across your organization.
