Data Models

How we perceive the data
Not the storage model
Typically, the relational data model

Aggregates

Unit for data manipulation
Unit of distribution
Transactional boundary
Relationships are not enforced
Denormalization
Typically, no atomic operations spaning multiple aggregates (exception: RavenDB)
Schemaless

Key-value and document stores

Lookup by ID
The value can be opaque (key-value) or used by queries (document)
For key-value stores, you can integrate search tools for query support
Model for data access

Column-family stores

Confusing model
Sparse table: columns can be added to any row, and rows can have different columns
Two-level map: first key identifies the row, second one identifies the column
Column families (super-columns in Cassandra)
Storage model more suited for reading
Columns are ordered (name, timestamp, etc)
Model for data access

Materialized views

Cached queries
Can be stale
Can be included in a document
Map-reduce

Graphs

Small records with complex interconnections
Nodes connected by edges
More performant than relational databases
Not suitable for clusters
Transactions need to span multiple nodes

Facts

Immutability
Time
Storage is cheap
Event sourcing
Datomic (Memory Image)