Databases Reference
In-Depth Information
FIGURE 10.8
Conceptual Big Data appliance.
The Big Data appliance is geared to answer some key areas that emerge as risks or challenges
when dealing with extremely large data processing. The primary areas include data loading,
availability, data volume, storage performance, scalability, diverse and changing query demands
against the data, and operational costs of the next-generation data warehouse platform. The risks
can be applied to both the structured and the unstructured data that will be coexisting in this
platform.
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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—the appliance architecture enables a quick way to calculate the total cost of
ownership and especially operational costs, since the configuration of the appliance is focused on
satisfying all the known requirements as documented.
The areas discussed here are some key considerations for looking at the appliance as a solution
rather than building out your own architecture.
Workload processing in this architecture is configured to the requirements as specified by the
users, including data acquisition, usage, retention, and processing. The complexity of this architecture
is the configuration and initial setup, which will need significant rework if the specifications are not
clear or tend to change over time, since the initial configuration is customized.
