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Data Science
Architecture
Data Mesh
Decentralized domain-oriented data architecture treating data as a product.
Intent & Description
📋 Context
Traditional centralized data platforms struggle to scale with organizational growth. Data mesh distributes data ownership to domain teams who provide data as products.
Real-world Use Case
Large organizations with multiple domains where teams need autonomy over their data while maintaining cross-domain interoperability.
Source
Advantages
- Scalable across domains
- Reduces bottleneck of centralized teams
- Domain-specific expertise applied to data
- Faster time-to-value for data products
Disadvantages
- Requires organizational change
- Coordination overhead
- Standardization challenges
- Discovery and governance complexity
Implementation Example
# Data Mesh Concept class DataProduct: def __init__(self, domain, owner): self.domain = domain self.owner = owner self.consumers = []
def register_consumer(self, consumer): self.consumers.append(consumer)
# Domain teams own their data products sales_data = DataProduct("sales", "sales_team") marketing_data = DataProduct("marketing", "marketing_team")