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DBMS > Datomic vs. Hazelcast vs. ObjectBox vs. Vertica

System Properties Comparison Datomic vs. Hazelcast vs. ObjectBox vs. Vertica

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonHazelcast  Xexclude from comparisonObjectBox  Xexclude from comparisonVertica infoOpenText™ Vertica™  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityA widely adopted in-memory data gridExtremely fast embedded database for small devices, IoT and MobileCloud 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.
Primary database modelRelational DBMSKey-value storeObject oriented DBMSRelational DBMS infoColumn oriented
Secondary database modelsDocument store infoJSON support with IMDG 3.12Time Series DBMSSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score5.46
Rank#61  Overall
#7  Key-value stores
Score1.29
Rank#166  Overall
#5  Object oriented DBMS
Score10.06
Rank#42  Overall
#26  Relational DBMS
Websitewww.datomic.comhazelcast.comobjectbox.iowww.vertica.com
Technical documentationdocs.datomic.comhazelcast.org/­imdg/­docsdocs.objectbox.iovertica.com/­documentation
DeveloperCognitectHazelcastObjectBox LimitedOpenText infopreviously Micro Focus and Hewlett Packard
Initial release2012200820172005
Current release1.0.6735, June 20235.3.6, November 202312.0.3, January 2023
License infoCommercial or Open Sourcecommercial infolimited edition freeOpen Source infoApache Version 2; commercial licenses availableOpen Source infoApache License 2.0commercial infoLimited community edition free
Cloud-based only infoOnly available as a cloud servicenononono infoon-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureJavaC and C++C++
Server operating systemsAll OS with a Java VMAll OS with a Java VMAndroid
iOS
Linux
macOS
Windows
Linux
Data schemeyesschema-freeyesYes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.
Typing infopredefined data types such as float or dateyesyesyesyes
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.noyes infothe object must implement a serialization strategynono
Secondary indexesyesyesyesNo Indexes Required. Different internal optimization strategy, but same functionality included.
SQL infoSupport of SQLnoSQL-like query languagenoFull 1999 standard plus machine learning, time series and geospatial. Over 650 functions.
APIs and other access methodsRESTful HTTP APIJCache
JPA
Memcached protocol
RESTful HTTP API
Proprietary native APIADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL infocharacter-based, interactive, front-end utility
Supported programming languagesClojure
Java
.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala
C
C++
Dart
Go
Java
JavaScript infoplanned (as of Jan 2019)
Kotlin
Python infoplanned (as of Jan 2019)
Swift
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Server-side scripts infoStored proceduresyes infoTransaction Functionsyes infoEvent Listeners, Executor Servicesnoyes, PostgreSQL PL/pgSQL, with minor differences
TriggersBy using transaction functionsyes infoEventsnoyes, called Custom Alerts
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingnonehorizontal partitioning, hierarchical partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersyes infoReplicated Maponline/offline synchronization between client and serverMulti-source replication infoOne, or more copies of data replicated across nodes, or object-store used for repository.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnono infoBi-directional Spark integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency selectable by user infoRaft Consensus AlgorithmImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDone or two-phase-commit; repeatable reads; read commitedACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
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 developmentyesnono
User concepts infoAccess controlnoRole-based access controlyesfine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash
More information provided by the system vendor
DatomicHazelcastObjectBoxVertica infoOpenText™ Vertica™
Specific characteristicsDeploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more
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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