Modeling and Management of Big Data in Databases

The existing paradigms for dealing with regular data are neither enough nor suitable to deal with Big Data requirements. For that reason, at the data storage level, the introduction of novel approaches, such as the NoSQL databases, is required. NoSQL refers to Not Only SQL, the term used for all the non-relational databases. NoSQL databases are considered schema-less, as they are designed to work without structure; however, in practice, there is a need for a self-sufficient model to define how data will be organized and retrieved from the database. To solve this requirement, some diverse NoSQL data models are proposed.

Data Models

A data model is a representation of the structure of the data for processing and organization. A data model is considered a primary element for storage, analysis and processing in storage systems.
Currently, storage systems are classified into two large groups, relational and non-relational. Within the relational, the well-known models are the Entity–Relationship (ER), Extended Entity Relationship (EER), Key-Cube and Multidimensional, among others. The objective of this article is not to present a deep study of the models considered as classic: they are well-known and do not need to be explained. We only develop a study of the models that are a novelty for Big Data.
For non-relational systems, there are the NoSQL databases; for them, the data models are classified into four main categories:

1. Column-oriented
2. Document-oriented
3. Graph
4. Key-value

Column-Oriented

In this model, data are represented in tabular form by columns and rows. The columns are identifiable by a partition key that is unique and mandatory and the rows by an optional clustering key. The primary key is the combination of the partition and clustering key. Basically, the schema of the tables consists of a set of columns, a primary key and a data type. For Database Management Systems (DBMS) that use the column-oriented data model, we can mention Accumulo, Amazon SimpleDB, Cassandra, Cloudata, Druid, Elassandra, Flink, HBase, Hortonworks, HPCC, Hypertable, IBM Informix, Kudu, MonetDB, Scylla and Splice Machine, among others.

Document-Oriented

In this model, data are stored in key-value pairs, value documents in XML, JSON or BSON formats. Each of the documents can have nested subdocuments, indexes, fields and attributes. As examples of DBMS that use the document-oriented data model, we can mention ArangoDB, Azure, BagriDB, Cloud Datastore, CouchDB, DocumentDB, Elastic, IBM Cloudant, MongoDB, NosDB, RavenDB, RethinkDB, SequoiaDB, ToroDB and UnQlite, among others.

Graph

This model consists of a graph that contains nodes and edges. A node represents an entity and an edge represents the relationship between entities. There are several graph structures: Undirected/directed, Labeled graphs, Attributed graphs, Bigdata, Multigraphs, Hypergraphs and Nested graphs, among others. Some examples of DBMS that use the graph data model are AllegroGraph, ArangoDB, Infinite Graph, GraphBase, HyperGraphDB, InfoGrid, Meronymy, Neo4j, Onyx Database, Titan, Trinity, Virtuoso OpenLink, Sparksee and WhiteDB.

Key-value

In this model, the data are represented by a key-value tuple. The key represents a unique identifier indexed to a value that represents data of arbitrary type, structure and size. Secondary keys and indexes are not supported. Aerospike, Azure Table Storage, BangDB, Berkeley DB, DynamoDB, GenieDB, KeyDB, Redis, Riak, Scalaris, Voldemort, among others are examples of DBMS that use the key-value data model.
Table 2 summarizes the main characteristics of NoSQL data models, such as its main concept, structure, techniques to create the data model, advantages and disadvantages.

Table 2. NoSQL characteristics.