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DBMS > Datomic vs. Vertica vs. Yanza

System Properties Comparison Datomic vs. Vertica vs. Yanza

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparisonYanza  Xexclude from comparison
Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityCloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.Time Series DBMS for IoT Applications
Primary database modelRelational DBMSRelational DBMS infoColumn orientedTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score10.68
Rank#43  Overall
#27  Relational DBMS
Websitewww.datomic.comwww.vertica.comyanza.com
Technical documentationdocs.datomic.comvertica.com/­documentation
DeveloperCognitectOpenText infopreviously Micro Focus and Hewlett PackardYanza
Initial release201220052015
Current release1.0.6735, June 202312.0.3, January 2023
License infoCommercial or Open Sourcecommercial infolimited edition freecommercial infoLimited community edition freecommercial infofree version available
Cloud-based only infoOnly available as a cloud servicenono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containersno infobut mainly used as a service provided by Yanza
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureC++
Server operating systemsAll OS with a Java VMLinuxWindows
Data schemeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.schema-free
Typing infopredefined data types such as float or dateyesyesno
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nonono
Secondary indexesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.no
SQL infoSupport of SQLnoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.no
APIs and other access methodsRESTful HTTP APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
HTTP API
Supported programming languagesClojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
any language that supports HTTP calls
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes, PostgreSQL PL/pgSQL, with minor differencesno
TriggersBy using transaction functionsyes, called Custom Alertsyes infoTimer and event based
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peershorizontal partitioning, hierarchical partitioningnone
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono infoBi-directional Spark integrationno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentno
User concepts infoAccess controlnofine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hashno
More information provided by the system vendor
DatomicVertica infoOpenText™ Vertica™Yanza
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
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Competitive advantagesFast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
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Typical application scenariosCommunication and network analytics, Embedded analytics, Fraud monitoring and Risk...
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Key customersAbiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
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Licensing and pricing modelsCost-based models and subscription-based models are both available. One license is...
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DatomicVertica infoOpenText™ Vertica™Yanza
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