Types of the databases: detailed overview of the DBMS types

Often, in the reviews of the database types, there is information about relational and “other” types, “NoSQL” etc. or the most basic types of DBMSs (databases), the rare ones are not mentioned t all. In this article we will try to describe all the types of the databases and give examples of the specific implementations. Of course, the article does not pretend to be all-embracing, databases can be classified in different ways, including types of optimal load, etc. Moreover, all the types of the databases will be described briefly. But we really hope that the article will give basic understanding of the DBMS types as well as of the principles of their operation.

In this article we are going to talk about the following types of the databases:

• Relational
• Key-value
• Document-oriented
• Time series databases
• Graph Databases
• Search Engines
• Object-oriented databases
• RDF (Resource Description Framework)
• Wide Column Stores
• Multimodal DBMS
• Native XML DBMS
• GEO/GIS (spatial) and specialized DBMSs
• Event DBMS (state transition databases)
• Content DBMSs
• Navigational DBMSs
• Vector databases

Let’s start with the most common type – relational DBMSs.

Relational DBs

The most well-known relational databases are Open Source projects PostgreSQL, MySQL and SQLite, as well as proprietary solutions such as Oracle, Microsoft SQL Server and IBM Db2Relational.

The essence of relational databases consists in data storage in the linked tables:

It is also worth saying that the relational databases come with row-based (PostgreSQL) and column-based (ClickHouse, Vertica) data storage. Column databases are better for analytics purposes, while row orientation database is better for transactional workloads.

Relational DBMSs are the most common type of the databases. The table with more than 150 relational variants is given below. The source of this list is a special site-agregator.

Full list of 166 relational DBMSs

1. Oracle – relational, multimodal
2. MySQL – relational, multimodal
3. Microsoft SQL Server – relational, multimodal
4. PostgreSQL – relational, multimodal
5. IBM Db2Relational – multimodal
6. Microsoft Access – relational
7. SQLite – relational
8. Snowflake – relational
9. MariaDB – relational, multimodal
10. Microsoft Azure SQL Database – relational, multimodal
11. Hive – relational
12. Databricks – multimodal
13. Teradata – relational, multimodal
14. Google BigQuery – relational
15. FileMaker – relational
16. SAP HANA – relational, multimodal
17. SAP Adaptive Server – relational
18. Microsoft Azure Synapse Analytics – relational
19. Firebird – relational
20. Informix – relational, multimodal
21. Amazon Redshift – relational
22. Impala – relational, multimodal
23. Spark SQL – relational
24. ClickHouse – relational, multimodal
25. Netezza – relational
26. Vertica – relational
27. Presto – relational
28. dBASE – relational
29. Apache Flink – relational
30. Greenplum – relational, multimodal
31. Amazon Aurora – relational, multimodal
32. H2 – relational, multimodal
33. Oracle Essbase – relational
34. Microsoft Azure Data Explorer – relational, multimodal
35. Microsoft Azure Data Explorer – relational, multimodal
36. CockroachDB – relational
37. Derby – relational
38. Interbase – relational
39. Trino – relational, multimodal
40. SingleStore – relational, multimodal
41. SAP SQL Anywhere – relational
42. Ingres – relational
43. HyperSQL – relational
44. Ignite – multimodal
45. SAP IQ – relational
46. Virtuoso – multimodal
47. OpenEdge – relational
48. Oracle NoSQL – multimodal
49. Google Cloud Spanner – relational
50. YugabyteDB – relational, multimodal
51. MaxDB -relational
52. TiDB – relational, multimodal
53. Apache Druid – multimodal
54. InterSystems Caché – multimodal
55. InterSystems IRIS – multimodal
56. DuckDB – relational
57. SAP Advantage Database Server – relational
58. HEAVY.AI – relational, multimodal
59. 4D – relational
60. Percona Server for MySQL – relational
61. EDB Postgres – relational
62. Apache Drill – multimodal
63. EXASOL – relational
64. Apache Phoenix – relational
65. Citus – relational, multimodal
66. Datomic – relational
67. Empress – relational
68. GridGain – multimodal
69. OceanBase – relational, multimodal
70. MonetDB – relational, multimodal
71. VoltDB – relational
72. Tibero – relational
73. TimesTen – relational
74. IBM Db2 warehouse -relational
75. SQLBase – relational
76. Firebolt – relational
77. Fauna – multimodal
78. mSQL – relational
79. MatrixOne – relational
80. DataEase – relational
81. Oracle Rdb – relational
82. Altibase – relational
83. PlanetScale – relational, multimodal
84. NonStop SQL – relational
85. Cubrid – relational
86. Infobright – relational
87. Apache Kylin – relational
88. GBase – relational
89. Apache HAWQ – relational
90. NuoDB – relational
91. Dolt – relational, multimodal
92. solidDB – relational
93. FoundationDB – multimodal
94. 1010data – relational
95. openGauss – relational, multimodal
96. HFSQL – relational
97. Actian Vector – relational
98. SQL.JS -relational
99. OpenBase – relational
100. Vitess – relational, multimodal
101. Kognitio – relational
102. StarRocks – relational
103. TDSQL for MySQL – relational, multimodal
104. DBISAM – relational
105. FrontBase – relational
106. TypeDB -multimodal
107. NexusDB – relational
108. Datacom/DB – relational
109. Kinetica – relational, multimodal
110. eXtremeDB – multimodal
111. ScaleArc – relational
112. VistaDB – relational
113. Yellowbrick – relational
114. Splice Machine – relational
115. Postgres-XL – relational, multimodal
116. Alibaba Cloud MaxCompute – relational
117. AlaSQL – multimodal
118. Apache Pinot – relational
119. Alibaba Cloud AnalyticDB for MySQL – relational, multimodal
120. SQream DB – relational
121. Sequoiadb – multimodal
122. Kingbase – relational, multimodal
123. Trafodion – relational
124. R:BASE – relational
125. Apache Doris – relational
126. Transbase – relational
127. Lovefield – relational
128. Raima Database Manager – multimodal
129. Alibaba Cloud AnalyticDB for PostgreSQL – relational
130. Tajo – relational
131. Mimer SQL – relational
132. Kyligence Enterprise – relational
133. YDB – multimodal
134. Databend – relational
135. Actian PSQL – relational
136. Alibaba Cloud ApsaraDB for PolarDB – relational
137. Brytlyt – relational
138. XtremeData – relational
139. TransLattice – relational
140. ElevateDB – relational
141. Comdb2 – relational
142. Linter – relational, multimodal
143. AntDB – relational
144. FeatureBase – relational
145. LeanXcale – multimodel
146. PipelineDB – relational
147. GeoSpock – relational, multimodal
148. Faircom DB – multimodal
149. Tibco ComputeDB – relational
150. Valentina Server – relational
151. PieCloudDB -relational
152. BigObject – relational
153. Edge Intelligence – relational
154. Fujitsu Enterprise Postgres – relational, multimodal
155. EsgynDB – relational
156. Transwarp KunDB – relational
157. JethroData – relational
158. MyScale – multimodal
159. OushuDB – relational
160. AgensGraph – multimodal
161. SmallSQL – relational
162. ActorDB – relational
163. DaggerDB – relational
164. EdgelessDB – relational
165. K-DB – relational
166. Sadas Engine – relational

Among the “domestic” players we can highlight the following:

• Postgres Pro – PostgreSQL finalized for corporate tasks;
  • Jatoba – similar to variant mentioned above as it is based on PostgreSQL;
  • Quantum Hybrid – one more PostgreSQL variant;
  • Red – a DBMS based on Interbase/Firebird;
  • ProximaBD – based on PostgreSQL;
  • Arenadata DB – enterprise solution based on Greenplum;
  • YDB – serverless solution by the Yandex company. This is an Open Source product which is available for on-premise installations and as a managed service with dedicated/serverless consumption model.

Relational databases cover a wide range of tasks: from transactional databases to analytical ones, but they are not a “silver bullet” for all the tasks. Consider other types of the databases as well.

Key-value DBs

Key-value database type is designed to perform fast, almost instantaneous queries for such tasks as cache, balance display etc. The high speed is realized due to the data storage in accordance with the key-value principle, and in most cases it is effected by operations in RAM.

Dictionaries contain a collection of objects or records; objects contain many different fields, containing data (each of them). Records are stored and retrieved by using a key that uniquely identifies the record and is used for fast data retrieval.

The main purpose is to speed up data mapping for the end users and reduce loads, including I/O on organizations’ infrastructure.

The most well-known and widely used Key-Value solutions are Redis and Memcached.

The full list of 72 Key-Value DBMSs is given below:

1. Redis – Key-value, multimodal
2. Amazon DynamoDB – multimodal
3. Microsoft Azure Cosmos DB – multimodal
4. Memcached – Key-value
5. etcd – Key-value
6. Hazelcast – Key-value, multimodal
7. Aerospike – multimodal
8. Ehcache – Key-value
9. Riak KV – Key-value
10. Ignite – multimodal
11. OrientDB – multimodal
12. Google Cloud Bigtable – multimodal
13. GemFire – Key-value, multimodal
14. ArangoDB – multimodal
15. Infinispan – Key-value
16. Oracle NoSQL – multimodal
17. RocksDB Key-value
18. Oracle Berkeley DB – multimodal
19. InterSystems Caché – multimodal
20. InterSystems IRIS – multimodal
21. LevelDB – Key-value
22. LMDB – Key-value
23. Geode – Key-value
24. Amazon SimpleDB – Key-value
25. Geode – Key-value
26. Amazon SimpleDB – Key-value
27. Oracle Coherence – Key-value
28. GridGain – multimodal
29. Tarantool – Key-value, multimodal
30. GT.M – Key-value
31. ZODB – Key-value
32. FoudationDB – multimodal
33. NCache – Key-value
34. WebSphere eXtreme Scale – Key-value
35. Hibari – Key-value
36. MapDB – Key-value
37. BoltDB – Key-value
38. Graph Engine – multimodal
39. Scalaris – Key-value
40. KeyDB – Key-value
41. Project Voldemort – Key-value
42. Upscaledb – Key-value
43. Cloudflare Workers KV – Key-value
44. Elliptics –  Key-value
45. Tokyo – Tyrant – Key-value
46. LeanXcale – multimodal
47. Immudb – Key-value, multimodal
48. STSdb – Key-value
49. TomP2P – Key-value
50. ArcadeDB – multimodal
51. Speedb – Key-value
52. Faircom DB – multimodal
53. Kyoto Tycoon – Key-value
54. Skytable – Key-value
55. HyperLevelDB – Key-value
56. YTsaurus – multimodal
57. InfinityDB – Key-value
58. Tigris – multimodal
59. Badger – Key – value
60. Dragonfly – Key – value
61. LedisDB – Key-value
62. TerarkDB – Key-value
63. Cachelot.io – Key-value
64. ScaleOut StateServer – Key-value
65. JaguarDB – Key-value
66. Resin Cache – Key-value
67. Faircom EDGE – multimodal
68. SwayDB – Key-value
69. BergDB – Key-value
70. CortexDB – multimodal
71. Helium – Key-value
72. Tkrzw – Key-value

Among the domestic DBMSs firsr of all we should highlight Tarantool, distributed under the Simplified BSD license, it has a separate version for large corporate clients. Tarantool implements a hybrid data schema: key-value, document-oriented, relational and spatial.

Document-oriented DBs

If you need to store a lot of files or documents and you don’t want to think  about storage structure, hierarchy, relationships etc. we suggest choosing one of the document-oriented databases. The main advantage is the wide scalability.

Document-oriented databases are created for the hierarchical data structures (documents) storage. The basis of document-oriented DBMSs are document storages with a tree or forest structure. Trees start from the root node and may contain several internal and leaf nodes. Leaf nodes contain the data, which are entered into the indexes while adding a document; this makes it possible to find the path to the searched data even with a rather complex structure. Unlike the key-value type storages, the selection on the request to the document storage can contain parts of a large number of documents without full loading of these documents into the RAM.

The most popular document-oriented database is MongoDB.

Full list of 56 document-oriented database solutions is given below:

1. MongoDB – document-oriented, multimodal
2. Amazon DynamoDB – multimodal
3. Databricks – multimodal
4. Microsoft Azure Cosmos DB – multimodal
5. Couchbase – document-oriented, multimodal
6. Firebase Realtime Database – document-oriented
7. CouchDB – document-oriented, multimodal
8. Google Cloud Firestore – document-oriented
9. MarkLogic – multimodal
10. Realm – document-oriented
11. Aerospike – multimodal
12. Google Cloud Datastore – document-oriented
13. Virtuoso – multimodal
14. OrientDB – multimodal
15. ArangoDB – multimodal
16. RavenDB – document-oriented, multimodal
17. Oracle NoSQL – multimodal
18. IBM Cloudant – document-oriented
19. RethinkDB – document-oriented, multimodal
20. InterSystems IRIS – multimodal
21. PouchDB – document-oriented
22. CloudKit – document-oriented
23. Apache Drill – multimodal
24. Amazon DocumentDB – document-oriented
25. Mnesia – document-oriented
26. LiteDB – document-oriented
27. Fauna – multimodal
28. Datameer – document-oriented
29. GigaSpaces – multimodal
30. FoundationDB – multimodal
31. AllegroGraph – multimodal
32. HPE Ezmeral Data Fabric – multimodal
33. CrateDB – multimodal
34. LokiJS – document-oriented
35. BigchainDB – document-oriented
36. AlaSQL – multimodal
37. SurrealDB – multimodal
38. Sequoiadb – multimodal
39. Percona Server for MongoDB – document-oriented
40. HarperDB – document-oriented
41. EJDB – document-oriented
42. YDB – multimodal
43. ArcadeDB – multimodal
44. Bangdb – multimodal
45. XTDB – document-oriented
46. YTsaurus – multimodal
47. OrigoDB – multimodal
48. WhiteDB – document-oriented
49. ToroDB – document-oriented
50. SenseiDB – document-oriented
51. Acebase – document-oriented
52. iBoxDB – document-oriented
53. RaptorDB – document-oriented
54. NosDB – document-oriented
55. CortexDB – multimodal
56. JasDB – document-oriented

Among the domestic solutions, the Yenisei DBMS can be referred to the document-oriented databases.

And within one of the modalities, YDB and YTsaurus can be referred to such databases as well.

Time-series DBs

If you have time-ordered or time-stamped data, such as metrics from infrastructure or sensor data, it may be reasonable to use one of the time series databases.

General characteristics of the time-series databases:

• Time-series data are always collected over a period of time;
  • Data from workloads are new and are recorded as inserts. Existing data are not updated by replacement;
  • When data are written, they are automatically assigned to the last time interval.

Time-series databases are often used to monitor various metrics (CPU utilization, sensor performance etc.).

The most popular time-series databases are Prometheus, InflubDB, Graphite.

Full list of 43 time-series DBMSs  is given below:

1.	InfluxDB	Time Series, multimodal
2.	Kdb	multimodal
3.	Prometheus	Time Series
4.	Graphite	Time Series
5.	TimescaleDB	Time Series, multimodal
6.	DolphinDB	Time Series, multimodal
7.	RRDtool	Time Series
8.	Apache Druid	multimodal
9.	TDengine	Time Series, multimodal
10.	QuestDB	Time Series, multimodal
11.	OpenTSDB	Time Series
12.	GridDB	Time Series, multimodal
13.	Fauna	multimodal
14.	VictoriaMetrics	Time Series
15.	Amazon Timestream	Time Series
16.	M3DB	Time Series
17.	Heroic	Time Series
18.	eXtremeDB	multimodal
19.	CrateDB	multimodal
20.	Apache IoTDB	Time Series
21.	KairosDB	Time Series
22.	ITTIA	Time Series,multimodal
23.	Raima Database Manager	multimodal
24.	Axibase	Time Series
25.	Riak TS	Time Series
26.	Machbase	Time Series
27.	CnosDB	Time Series
28.	ArcadeDB	multimodal
29.	Bangdb	multimodal
30.	IRONdb	Time Series
31.	Quasardb	Time Series
32.	SiteWhere	Time Series
33.	NSDb	Time Series
34.	Alibaba Cloud TSDB	Time Series
35.	IBM Db2 Event Store	multimodal
36.	Tigris	multimodal
37.	GreptimeDB	Time Series
38.	Hawkular Metrics	Time Series
39.	SiriDB	Time Series
40.	Blueflood	Time Series
41.	Warp 10	Time Series
42.	Yanza	Time Series
43.	Newts	Time Series

Graph DBs

If you need to analyze data relationships, their connections or just simplify queries with Join, it makes sense to use graph databases.

Data and their relationships are represented as vertices and edges of the graph, respectively.

Thus you can easily represent money transfers (to identify various fraudulent schemes), social network connections and the graph of communication between mobile network operators.

Graph databases are usually used in the financial sphere in order to detect various fraudulent schemes, but they also will be convenient for any other task where it is necessary to work with relations of the objects.

The most well-known Open Source solution is Neo4J, but there are a number of other high-quality alternatives.

Full list of 39 graph databases is given below:

1.	Neo4j	graph
2.	Microsoft Azure Cosmos DB	multimodal
3.	Virtuoso	multimodal
4.	OrientDB	multimodal
5 .	ArangoDB	multimodal
6.	Memgraph	graph
7.	GraphDB	multimodal
8.	Amazon Neptune	multimodal
9.	JanusGraph	graph
10.	Stardog	multimodal
11.	NebulaGraph	graph
12.	TigerGraph	graph
13.	Fauna	multimodal
14.	Giraph	graph
15.	Dgraph	graph
16.	AllegroGraph	multimodal
17.	Blazegraph	multimodal
18.	TypeDB	multimodal
19.	Graph Engine	multimodal
20.	SurrealDB	multimodal
21.	InfiniteGraph	graph
22.	FlockDB	graph
23.	HyperGraphDB	graph
24.	Fluree	graph
25.	AnzoGraph DB	multimodal
26.	Sparksee	graph
27.	HugeGraph	graph
28.	GraphBase	graph
29.	ArcadeDB	multimodal
30.	Bangdb	multimodal
31.	RDFox	multimodal
32.	Ultipa	graph
33.	TinkerGraph	graph
34.	TerminusDB	graph, multimodal
35.	AgensGraph	multimodal
36.	Galaxybase	graph
37.	VelocityDB	multimodal
38.	HGraphDB	graph
39.	Transwarp StellarDB	graph

Search Engines

If you need to search large amounts of data, especially unstructured data, it will be very useful to work with  a database that would combine the functionality of text search with the functionality of information storage.

Let’s imagine that you have N petabytes of logs (or other text data). Ordinary word search is no longer suitable in this case.

Indexing is a great solution. If we consider it in a very exaggerated way, we can visualize it as follows: we assign an index to each word/term/n-gram and record these indices in a special table, where the rows are the document and the columns are the indices. A similar system is used for plagiarism search, but in this case shingles (indexes with layering) are used.

It is much faster to search by index than by matching words in the documents.

Of course, modern search engines offer wider functionality.

The most popular solutions of this kind are Elasticsearch (and its version – OpenSearch, Splunk (I wrote about it in one of the previous articles) and Sphinx.

Full list of search engines is given below:

1.	Elasticsearch	Search engine, multimodal
2.	Splunk	Search engine
3.	Solr	Search engine, multimodal
4.	OpenSearch	Search engine, multimodal
5.	MarkLogic	multimodal
6.	Algolia	Search engine
7.	Microsoft Azure Search	Search engine
8.	Sphinx	Search engine
9.	Virtuoso	multimodal
10.	ArangoDB	multimodal
11.	Coveo	Search engine
12.	Amazon CloudSearch	Search engine
13.	Meilisearch	Search engine
14.	Xapian	Search engine
15.	SearchBlox	Search engine
16.	Typesense	Search engine
17.	Vespa	multimodal
18.	Marqo	Search engine
19.	Alibaba Cloud Log Service	Search engine
20.	Manticore Search	Search engine, multimodal
21.	Exorbyte	Search engine
22.	Tigris	мультимодальная
23.	Indica	Search engine
24.	Rizhiyi	Search engine, multimodal
25.	searchxml	multimodal

Object-oriented DBs

Object-oriented databases are databases where information is represented as objects, just like in object-oriented programming languages.

Object-oriented databases have appeared as a way of a code native communication which is written using object-oriented languages with a database.

Object-oriented databases have the following advantages:

• There is no problem of data model mismatch between the data model in the database and the application because the database stores the data in the same form;
  • There is no need to maintain the data model on the database side separately;
  • All the objects at the source level are strictly typed.

The best known object-oriented DBMS is Db4o.

Full list of these DBs is given below:

1. InterSystems Caché – multimodal
2. Db4o – object-oriented
3. Actian NoSQL Database – object-oriented
4. ObjectStore – object-oriented
5. Matisse – object-oriented
6. Perst – object-oriented
7. GigaSpaces – multimodal
8. ObjectBox – object-oriented, multimodal
9. ObjectDB – object-oriented
10. atoti – object-oriented
11. GemStone/S – object-oriented
12. Objectivity/DB – object-oriented
13. Starcounter – object-oriented
14. Jade – object-oriented
15. Actian Fast – object-oriented
16. Eloquera – object-oriented
17. Siaqodb – object-oriented
18. OrigoDB – multimodal
19. DataFS – object-oriented, multimodal
20. WakandaDB – object-oriented
21. VelocityDB – multimodal

RDF (Resource Description Framework)

RDF databases are partly similar to graph databases. Their main function is based on the concept of formulating resource-related statements as subject-predicate-object expressions. The subject denotes the resource and the predicate denotes the features of the resource and defines the relationship between the subject and the object.

Here’s what Wikipedia says about RDF:

Resource Description Framework (RDF, “resource description framework”) is a model developed by the World Wide Web Consortium for representing data, especially metadata. RDF represents statements about resources in a form suitable for machine processing. RDF is part of the semantic web concept.

A resource in RDF can be any entity, whether informational (e.g. a website or an image) or non-informational (e.g. a person, a city or some abstract concept). A statement made about a resource has the form “subject – predicate – object” and is called a triplet. The statement “the sky is blue” in RDF terminology can be represented as follows: subject – “the sky”, predicate – “has a colour”, object – “blue”. URIs are used to denote subjects, relations and objects in RDF.

A set of RDF statements forms an oriented graph in which the vertices are subjects and objects and the edges represent relations.

RDF itself is not a file format, but an abstract data model, i.e. it describes the proposed structure, processing and interpretation of data. A number of record formats exist for storing and transmitting information stacked in the RDF model.

For processing RDF-data it is proposed to implement query languages such as SPARQL (W3C standard), RQL, RDQL.”

Full list of the DBs is given below:

1. MarkLogic – multimodal
2. Apache Jena TDB – RDF
3. Virtuoso – multimodal
4. GraphDB – multimodal
5. Amazon Neptune – multimodal
6. Stardog – multimodal
7. AllegroGraph – multimodal
8. Blazegraph – multimodal
9. RDF4J  – RDF
10. Redland – RDF
11. Strabon – RDF
12. 4store – RDF
13. Mulgara – RDF
14. CubicWeb – RDF
15. RedStore – RDF
16. AnzoGraph DB – multimodal
17. BrightstarDB – RDF
18. Dydra – RDF
19. RDFox – multimodal
20. SparkleDB RDF

Wide Column Stores

Wide Column Stores is a NoSQL database that stores data in flexible columns that can be distributed across multiple servers or database nodes, using multidimensional mapping to reference data by column, row and timestamps.

The advantages of Wide Column Stores databases include query speed, scalability and a flexible data model.

The main difference in comparison with relational databases is the following:

A relational database management system stores data in a table with rows that span multiple columns. If one row requires an additional column, that column must be added to the entire table with NULL or default values for all other rows. If you need to query this DBMS table for a value that is not indexed, scanning the table to find these values will be extremely slow.

Wide Column DBMSs have the concept of rows like relational databases, but reading or writing a row of data consists of reading or writing individual columns. A column is written only if there is a data element for it. Each data item can be referenced by a row key, the value query is optimized in the same way as an index query in a DBMS.

The first column model is an entity/attribute/value table. Inside each entity (columns) there is a table of values/attributes.

The Wide Column DBMS concept allows building solutions for processing really large amounts of data.

The most well-known implementations are Cassandra and HBase DBMSs.

Full list is given below:

1. Cassandra – Wide column, multimodal
2. HBase – Wide column
3. Microsoft Azure Cosmos DB – Multimodal
4. Datastax Enterprise – Wide column, multimodal
5. ScyllaDB – Wide column, multimodal
6. Microsoft Azure Table Storage – Wide column
7. Google Cloud Bigtable – Multimodal
8. HPE Ezmeral Data Fabric – multimodal
9. Amazon Keyspaces – Wide column
10. Elassandra – Wide column, multimodal
11. Alibaba Cloud Table Store – Wide column
12. SWC-DB – Wide column, multimodal
13. Accumulo – Wide column

Multimodal DBMSs

It is worth saying that each multimodal DBMS can ultimately be reduced to one or more of the types on which they are based. Namely, it can be based on a relational model or on a document-oriented model etc. But they also allow emulating additional types in terms of user interaction with them.

Full list of the DBMSs is given below:

1. Adabas – multimodal
2. UniData – multimodal
3. jBASE – multimodal
4. Northgate Reality – multimodal
5. Model 204 – multimodal
6. D3 – multimodal
7. SciDB – multimodal
8. OpenInsight – multimodal
9. Rasdaman – multimodal
10. OpenQM – multimodal

Native XML DBMSs

The Native XML Database type is based on the use of internal XML representation in contrast to XML add-ons over existing relational databases (XML enabled DB), which implement XML-SQL according to the SQL-2003 access standard.

Native XML DBMSs are designed to manage a large number of XML documents.

XML native DB (NXD) uses an internal representation  of the DOM XML in the database and has the following features:

• The minimal model includes: elements, attributes, PCDATA sections and document list;
  • An XML document is represented as a fundamental part of the repository (like a table in a relational database);
  • To access the XML native DB information store the XQuery query language should be used.

Full list of the Native XML DBMSs is given below:

1. MarkLogic – multimodal
2. Virtuoso – multimodal
3. Oracle Berkeley DB – multimodal
4. BaseX – Native XML
5. Sedna – Native XML
6. eXist-db – Native XML
7. searchxml – multimodal

GEO/GIS and specialized DBMSs

If you are working at a map service or a specialized application that uses location data, it is logical to use a specialized DBMS.

Unlike other types of databases designed for processing numeric and symbolic information, spatial databases have the ability to work with complete spatial objects, combining both traditional and geometric types of data. Spatial databases allow us to perform analytical queries containing spatial operators for analyzing spatial and logical relations of objects (“intersects…”, “touches…”, “is contained in…”, “contains…”, “is at a given distance from…”, “coincides…” and others).

Full list of the DBMSs is given below:

1. PostGIS – Spatial DBMS, multimodal
2. Aerospike – multimodal
3. SpatiaLite – Spatial DBMS, multimodal
4. GeoMesa – Spatial DBMS
5. H2GIS – Spatial DBMS, multimodal
6. SpaceTime – Spatial DBMS, multimodal

Event DBMSs

Let’s imagine that the data in your DBMS has changed and you want to understand the event that changed it as well as to view the history of changes and what led to it.

Event databases store not only the data itself, but also the flow/sequence of its changes and the events that caused these changes.

This format can be useful both for incident investigation and for researching business and information processes occurring in an enterprise.

Here is a list of well-known event databases:

1. EventStoreDB – Event
2. NEventStore – Event
3. IBM Db2 Event Store – multimodal

Content DBMSs

Content databases are the implementations of content repositories. They can be used to store and manage different content (photos, texts, code repositories, etc.).

Here are some examples of content databases:

1. Jackrabbit – content
2. ModeShape – content

Navigational DBMSs

Warning! Navigational databases have nothing to do with spatial databases. A navigational database is a type of database in which records or objects are found primarily by links from other objects. In some ways, it is close to the concept of graph databases.

A navigational database is a combination of a hierarchical and network model of database interfaces. Navigation methods use “pointers” and “paths” to navigate among the data records. The opposite model is the relational model, which uses “declarative” methods in which you ask the system what you want rather than how to get to it.

Here are some examples of navigational databases:

1. IMS – navigational
2. IDMS – navigational

Vector DBs

If you are engaged in machine learning, the main thing you have to work with is vectors. These can be rows from a table corresponding to the features of an object, or an embedding obtained at the output of a neural network that characterizes the processed object and is subsequently used for face identification or text translation.

Vector databases have been developed especially for this kind of tasks, allowing to work directly with vectors represented as numerical arrays.

The main advantage of such kind of the databases is the speed of search and processing of vectors.

Vector databases are a relatively new type of DBMS, it is difficult to name a leader in this category.

List of the vector DBMSs:

1. Kdb – multimodal
2. Pinecone – vector
3. Chroma – vector
4. Milvus – vector
5. Weaviate – vector
6. Vald – vector
7. Qdrant – vector
8. Deep Lake – vector
9. Vespa – multimodal
10. MyScale – multimodal

Final thoughts

In this article we have looked through the different types of database management systems, starting from the most popular types, such as relational databases, and ending by the very exotic ones, such as navigational databases. Each type has its own specialization and purposes. The main purpose of the article was to give you a general idea of the variety of the existing DBMS, more details will be given later.