Databases Reference
In-Depth Information
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Workloads Consideration:
transactional, analytical
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Data Store Design Considerations:
consistency, availability,
partition-tolerance
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Data Design Principle Consideration:
ACID, CAP, BASE
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Contextualization/Association Consideration:
inter-element
relationship, semantics
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Compression Consideration:
ratio, performance overheads
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Serialization Consideration:
Read Only, Write Only, Read/Write
balance
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Data Content/Analysis Type Consideration:
Key-Value Pairs,
Document-Oriented, Graph-Centric
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Latency Consideration:
low, medium, high
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Network Performance Consideration:
memory, I/O, CPU,
network
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Security Consideration:
regulatory, access control, compliance,
privacy
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Data Platform Hosting Consideration:
physical, virtual,
private/public/hybrid cloud
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Data Quality Consideration:
High Quality, Mostly low focus on
quality
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Organization Adoption Maturity Consideration:
chasm, early
taker, entrenching, mainstream, laggard, obsoleting
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Big Data Product Support Consideration:
commercial, vendor,
community, forum, broker, standards, practices
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Skill Set Consideration:
competency, training, retooling,
constraints, resources, tools
The Hadoop suitability test will help you assess your business problem mapped to
all these parameters and a recommendation can be the drawn out of the test concerning
whether you should go for Hadoop or not.
