Data Mesh is a decentralized approach to data architecture that treats data as a “product” and assigns the ownership of data to cross-functional teams within an organization. Instead of having a centralized data team managing all the data integration, each domain team is responsible for its own data, ensuring data governance and quality at the source.
Process:
- Extract: Data is extracted by each domain team.
- Transform: Transformations are applied by the domain teams themselves, ensuring the data is clean and ready for consumption.
- Load/Serve: Data is made available as a product, often via APIs or data platforms, for consumption by other teams or systems.
Tools: Data mesh doesn’t rely on specific tools but involves platforms like Databricks, AWS Lake Formation, or Azure Data Lake for managing data access and sharing.
Use Case: This approach is suitable for large, complex organizations that need to scale data ownership and democratize data, avoiding bottlenecks caused by central teams. It’s beneficial in environments with multiple, independent data sources and stakeholders.