4. Key
Value
Pair
•Data stores as key/value pairs
(hashtable)
•Access data (values) by strings called
keys
•Data has no required format data
may have any format
•Value can be JSON, BLOB, string,
number
•Operations: Insert(key,value),
Fetch(key), Update(key), Delete(key)
•E.g. Redis, Dynamo
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5. Column-
based
•Work on columns (lowest/smallest
instance of data)
•High performance on operations like
SUM, COUNT, AVG
•Widely used to manage data
warehouses, CRM, Library card
catalogs
•Examples: HBase, Cassandra
6. Document-
Oriented
•Pair each key with data
structure.
•Indexes are done via B-Trees.
•Documents can contain many
different key-value pairs, or key-
array pairs, or even nested
documents.
•Examples: MongoDB
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7. Graph-
Based
•Based on Graph Theory.
•Stores entities and
relations among those
entities.
•Graph database is a multi-
relational in nature.
•You can use graph
algorithms easily
•E.g. Neo4J, FlockDB
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8. CAP
Theorem
It is impossible for a
distributed data store to
offer more than two out
of three guarantees.
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9. Eventual
Consistency
•Means to have copies of data on
multiple machines to get high availability
and scalability.
•Data replication may not be
instantaneous but in sometime, they
become consistent. Hence, the name
eventual consistency.
•BASE: Basically Available, Soft state,
Eventual consistency
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