Editing Temporal database (section)

==Implementations in notable products==
The following implementations provide temporal features in a relational database management system (RDBMS).

* [[MariaDB]] version 10.3.4 added support for [[SQL:2011]] standard as "System-Versioned Tables".<ref>{{Cite web|url=https://mariadb.com/kb/en/library/system-versioned-tables/|title = System-Versioned Tables}}</ref>
* [[Oracle Database]]{{snd}} Oracle Workspace Manager is a feature of Oracle Database which enables application developers and DBAs to manage current, proposed and historical versions of data in the same database.
* [[PostgreSQL]] version 9.2 added native ranged data types that are capable of implementing all of the features of the pgFoundry temporal contributed extension.<ref>{{cite web|last1=Paquier|first1=Michael|title=Postgres 9.2 highlight: range types |url=http://michael.otacoo.com/postgresql-2/postgres-9-2-highlight-range-types/ |date=1 November 2012 |archive-url=https://web.archive.org/web/20160423215529/http://michael.otacoo.com/postgresql-2/postgres-9-2-highlight-range-types/ |archive-date=2016-04-23 |work=Michael Paquier<!-- skip this, as name already in, change to website url, or not, as seems down..?--> - Open source developer based in Japan}}</ref><ref>{{cite web|last1=Katz|first1=Jonathan S.|title=Range Types: Your Life Will Never Be The Same|url=https://wiki.postgresql.org/images/7/73/Range-types-pgopen-2012.pdf|access-date=14 July 2014}}</ref> The PostgreSQL range types are supported by numerous native operators and functions.
* [[Teradata]] provides two products. Teradata version 13.10 and [[Teradata#Teradata Database 14|Teradata version 14]] have temporal features based on TSQL2<ref>Al-Kateb, Mohammed et al. "[https://openproceedings.org/2013/conf/edbt/Al-KatebGCBCP13.pdf Temporal Query Processing in Teradata]". EDBT/ICDT ’13 March 18–22, 2013, Genoa, Italy</ref> built into the database.
* [[IBM Db2]] version 10 added a feature called "time travel query"<ref name="DB2">{{cite web|title=A matter of time: Temporal data management in DB2 10 |last1=Saracco|first1=Cynthia M. |last2=Nicola|first2=Matthias |last3=Gandhi|first3=Lenisha |website=[[IBM]] |date=3 April 2012 |url=http://www.ibm.com/developerworks/data/library/techarticle/dm-1204db2temporaldata/|archive-url=https://web.archive.org/web/20121025082348/http://www.ibm.com/developerworks/data/library/techarticle/dm-1204db2temporaldata/|language=en|access-date=2020-10-27|archive-date=2012-10-25}}</ref> which is based on the temporal capabilities of the [[SQL:2011]] standard.<ref name="SQL2011">Kulkarni, Krishna, and Jan-Eike Michels. "[https://sigmodrecord.org/publications/sigmodRecord/1209/pdfs/07.industry.kulkarni.pdf Temporal features in SQL: 2011]". ACM SIGMOD Record 41.3 (2012): 34-43.</ref>
* [[Microsoft SQL Server]] introduced Temporal Tables as a feature for SQL Server 2016. The feature is described in a video on Microsoft's "Channel 9" web site.<ref name="SQLServerVideo">{{Citation|title=Temporal in SQL Server 2016|url=https://channel9.msdn.com/Shows/Data-Exposed/Temporal-in-SQL-Server-2016|language=en|access-date=2019-07-19}}</ref>

Non-relational, NoSQL database management systems that provide temporal features including the following:
* [[TerminusDB]] is a fully featured [[Open-source software|open source]] [[graph database]] that natively supports version control, time-travel queries and diffing functions. It has an immutable layer architecture based on [[delta encoding]] and [[succinct data structure]]s.<ref>{{Cite web|title=terminusdb/terminusdb-server|url=https://github.com/terminusdb/terminusdb-server|access-date=2020-09-04|website=GitHub|language=en}}</ref> 
* [[MarkLogic]] introduced bitemporal data support in version 8.0. Time stamps for Valid and System time are stored in JSON or XML documents.<ref>{{cite web|last1=Bridgwater|first1=Adrian|title=Data Is Good, 'Bidirectionalized Bitemporal' Data Is Better|website=[[Forbes]] |url=https://www.forbes.com/sites/adrianbridgwater/2014/11/24/data-is-good-bidirectionalized-bitemporal-data-is-better/#5424ff9b567b |date=24 November 2014}}</ref>
* [https://sirix.io SirixDB] stores snapshots of (currently) XML- and JSON-documents very efficiently in a binary format due to a novel versioning algorithm called sliding snapshot, which balances read-/write-performance and never creates write peaks. Time-travel queries are supported natively as well as diffing functions.
* [https://github.com/xtdb/xtdb XTDB] (formerly Crux) provides point-in-time bitemporal [[Datalog]] queries over transactions and documents ingested from semi-immutable Kafka logs. Documents are automatically indexed to create [[Entity–attribute–value model]] indexes without any requirement to define a schema. Transaction operations specify the effective Valid times. Transaction times are assigned by Kafka and enable horizontal scalability via consistent reads.
* [https://github.com/RecallGraph/RecallGraph RecallGraph] is a point-in-time, unitemporal (transaction time) graph database, built on top of [[ArangoDB]]. It runs on ArangoDB's [https://www.arangodb.com/why-arangodb/foxx/ Foxx Microservice] sub-system. It features [[Version Control System|VCS]]-like semantics in many parts of its interface, and is backed by a [[Atomicity (database systems)|transactional]] event tracker. Bitemporality is listed as one of the items in its [https://github.com/RecallGraph/RecallGraph#development-roadmap development roadmap].
* [https://docs.datomic.com/cloud/index.html Datomic] "is a distributed database that provides ACID transactions, flexible schema, [...] Datalog queries, complete data history, and SQL analytics support." For every change made to the data, it records the responsible transaction and the point in time when it happened.<ref>{{cite web|title=Datomic Data Model: Time Model|url=https://docs.datomic.com/cloud/whatis/data-model.html#time-model|date=29 April 2024}}</ref>

Temporal databases were one of the earliest forms of [[data version control]], and influenced the development of modern data versioning systems.<ref>{{Cite arXiv |last1=Bhardwaj |first1=Anant |last2=Bhattacherjee |first2=Souvik |last3=Chavan |first3=Amit |last4=Deshpande |first4=Amol |last5=Elmore |first5=Aaron J. |last6=Madden |first6=Samuel |last7=Parameswaran |first7=Aditya G. |date=2014-09-02 |title=DataHub: Collaborative Data Science & Dataset Version Management at Scale |class=cs.DB |eprint=1409.0798 }}</ref> 

===Alternatives===
[[File:Scd model.png|thumb|Example of [[slowly changing dimension]] (SCD) model]]

[[Slowly changing dimension]]s can be used to model temporal relations.