Simple model.
• Able to distribute horizontally.
Cons:
• Many data structures (objects), hence cannot be easily modeled
as key–value pairs.
(b) BigTable
‘‘BigTable’’ databases are known as Record-oriented or Tabular
databases consisting of multiple Tables, each containing a set of
addressable rows. Each row consists of a set of values that are considered
columns. In addition to Google’s BigTable database other
examples are:
• Azure Tables (Microsoft).
• Cassandra (Apache).
• HBase (Apache Hadoop project).
• Hypertable.
• SimpleDB (Amazon).
• Voldemort (LinkedIn, now open source).
(c) Columnar databases
Columnar databases are a hybrid between NoSQL and relational
databases. They provide some row-and-column structure, but do
not have the strict rules of relational databases.
Column-oriented databases store and process data by column
instead of row. Having its origin in analytics and business intelligence,
column-stores can be used to build high-performance
applications. Column oriented [6] stores are seen less puristic,
subsuming data stores that integrate column and roworientation.
This is faster because most of the tables contain lots of
columns which are rarely used simultaneously by queries. Columnar
databases only perform I/O on the blocks corresponding to
columns that are actually being read/updated. In addition to the
smaller I/O overhead, memory is more efficiently utilized. The
column-oriented data store very effective in blocking data based
on each column’s data type such as Date, Text,etc.
(d) Document databases
In document oriented databases, data is stored as documents.
A set of documents is called collections. Collections may contain