The Types of Databases

Relational databases

A relational database is a type of database that stores and provides access to data points that are related to one another. Relational databases are based on the relational model, an intuitive, straightforward way of representing data in tables.

List of Relational databases

• Oracle
• MySQL
• SQL Server
• PostgreSQL
• IBM DB2
• Microsoft Access
• SQLite
• MariaDB
• Informix
• Azure SQL

NoSQL databases

A NoSQL database provides a mechanism for storage and retrieval of data that is modeled in means other than the tabular relations used in relational databases. Such databases have existed since the late 1960s, but the name “NoSQL” was only coined in the early 21st century, triggered by the needs of Web 2.0 companies.

List of NoSQL databases

• MongoDB
• Redis
• Cassandra
• HBase
• Neo4j
• Amazon DynamoDB
• CouchDB
• Memcached

Cloud databases

A cloud database is a database that is built, deployed, and accessed in a cloud environment, such as a private, public, or hybrid cloud.

List of Cloud databases

• Microsoft Azure
• Amazon Web Service (AWS)

1. Amazon RDS. Amazon Relational Database Service runs on either Oracle, SQL, or MySQL server instances.
2. Amazon SimpleDB. Designed for smaller workloads, SimpleDB is primarily a schema-less database.
3. Amazon DynamoDB. DynamoDB is a NoSQL database capable of automatically replicating workloads across three availability zones.

• Oracle
• Google Cloud
• IBM Db2 on Cloud
• MongoDB Atlas
• OpenStack
• DataStax Astra
• Rackspace
• Redis Enterprise Cloud
• EDB Postgres Advanced Server
• SAP HANA Cloud

Columnar databases

A columnar database is a database management system (DBMS) that stores data in columns instead of rows. The purpose of a columnar database is to efficiently write and read data to and from hard disk storage in order to speed up the time it takes to return a query. Columnar databases store data in a way that greatly improves disk I/O performance. They are particularly helpful for data analytics and data warehousing.

List of Columnar databases

• Amazon Redshift.
• Snowflake.
• Vertica.
• BigQuery.
• ClickHouse.
• Druid.
• Hbase.
• Apache Kudu.

Wide column databases

A wide-column database is a NoSQL database that organizes data storage into flexible columns that can be spread across multiple servers or database nodes, using multi-dimensional mapping to reference data by column, row, and timestamp.

List of Wide column databases

• Hadoop/Hbase
• Cassandra
• Hypertable
• Accumulo
• Amazon SimpleDB
• Cloud Data
• HPCC
• Flink

Object-oriented databases

An object-oriented database (OOD) is a database system that can work with complex data objects — that is, objects that mirror those used in object-oriented programming languages. In object-oriented programming, everything is an object, and many objects are quite complex, having different properties and methods.

List of Object-oriented databases

• IBM Db2.
• InterSystems IRIS.
• Google Cloud Storage for Firebase.
• InterSystems Caché
• Visual FoxPro

Key-value databases

A key–value database, or key–value store, is a data storage paradigm designed for storing, retrieving, and managing associative arrays, and a data structure more commonly known today as a dictionary or hash table.

List of Key-value databases

• Amazon DynamoDB.
• Amazon ElastiCache.
• Redis.
• Couchbase.
• ScyllaDB.
• Aerospike.
• Hbase.
• InterSystems IRIS

Hierarchical databases

A hierarchical database model is a data model in which the data are organized into a tree-like structure. The data are stored as records which are connected to one another through links. A record is a collection of fields, with each field containing only one value.

Document databases

A document-oriented database, or document store, is a computer program and data storage system designed for storing, retrieving and managing document-oriented information, also known as semi-structured data.

Document-oriented databases are one of the main categories of NoSQL databases, and the popularity of the term “document-oriented database” has grown with the use of the term NoSQL itself. XML databases are a subclass of document-oriented databases that are optimized to work with XML documents. Graph databases are similar, but add another layer, the relationship, which allows them to link documents for rapid traversal.

List of Document databases

• MongoDB
• Amazon DynamoDB
• Microsoft Azure Cosmos DB
• Couchbase
• Firebase Realtime Database
• CouchDB
• MarkLogic
• Realm
• Google Cloud Firestore
• ArangoDB

Graph databases

Graph databases are purpose-built to store and navigate relationships. Relationships are first-class citizens in graph databases, and most of the value of graph databases is derived from these relationships. Graph databases use nodes to store data entities, and edges to store relationships between entities. An edge always has a start node, end node, type, and direction, and an edge can describe parent-child relationships, actions, ownership, and the like. There is no limit to the number and kind of relationships a node can have.

A graph in a graph database can be traversed along specific edge types or across the entire graph. In graph databases, traversing the joins or relationships is very fast because the relationships between nodes are not calculated at query times but are persisted in the database. Graph databases have advantages for use cases such as social networking, recommendation engines, and fraud detection, when you need to create relationships between data and quickly query these relationships.

List of Graph databases

• Neo4j.
• ArangoDB.
• Dgraph.
• OrientDB.
• Amazon Neptune.
• DataStax.
• FlockDB.
• Cassandra.

Time series databases

A time series database is a software system that is optimized for storing and serving time series through associated pairs of time and value. In some fields, time series may be called profiles, curves, traces or trends.

List of Time series databases

• InfluxDB.
• Kdb+
• Prometheus.
• Graphite.
• TimescaleDB.
• Apache Druid.
• RRDTool.
• OpenTSDB.

Data Warehousing platform

Data warehousing improves access to information, speeds up query-response times, and allows businesses to fetch deeper insights from big data. Previously, companies had to invest a lot in infrastructure to build a data warehouse. The advent of cloud technology has significantly reduced the cost of data warehousing for businesses. Data warehousing database business analytics

List of Time series databases

• Amazon Redshift
• Microsoft Azure
• Google BigQuery
• Snowflake
• Teradata
• SAP HANA

Focus

• Relational databases

1. Oracle
2. MySQL
3. SQL Server
4. PostgreSQL

• NoSQL databases

1. MongoDB
2. Redis
3. Cassandra
4. Amazon DynamoDB

• Cloud databases

1. Microsoft Azure
2. Amazon Web Service (AWS)

• Columnar databases

1. Amazon Redshift.
2. Snowflake.
3. BigQuery.

• Wide column databases

1. Hadoop/Hbase
2. Cassandra

• Key-value databases

1. Amazon DynamoDB.
2. Amazon ElastiCache.
3. Redis.

• Time series databases

1. InfluxDB.
2. Prometheus.

• Data Warehousing platform

1. Amazon Redshift
2. Microsoft Azure
3. Google BigQuery
4. Snowflake
5. Teradata

Rajesh Kumar

I’m a DevOps/SRE/DevSecOps/Cloud Expert passionate about sharing knowledge and experiences. I have worked at Cotocus. I share tech blog at DevOps School, travel stories at Holiday Landmark, stock market tips at Stocks Mantra, health and fitness guidance at My Medic Plus, product reviews at TrueReviewNow , and SEO strategies at Wizbrand.

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