Databases Reference
In-Depth Information
FIGURE 10.6
External data integration.
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Modular data integration architecture.
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Heterogeneous physical architecture deployment, providing best-in-class integration at the
data processing layer.
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Cons:
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Data bus architecture can become increasingly complex.
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Poor metadata architecture can creep due to multiple layers of data processing.
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Data integration can become a performance bottleneck over a period of time.
Typical use cases for this type of integration architecture can be seen in organizations where the
data remains fairly stable over a period of time across the Big Data spectrum. Examples include
social media channels and sensor data.
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Data loading is isolated across the layers. This provides a foundation to create a robust data
management strategy.
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Data availability is controlled to each layer and security rules can be implemented to each layer as
required, avoiding any associated overhead for other layers.
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Data volumes can be managed across the individual layers of data based on the data type, the life-
cycle requirements for the data, and the cost of the storage.
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Storage performance is based on the data categories and the performance requirements, and the
storage tiers can be configured.
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Operational costs—in this architecture the operational cost calculation has fixed and variable cost
components. The variable costs are related to processing and computing infrastructure and labor
costs. The fixed costs are related to maintenance and data-related costs.
