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DBMS > Datomic vs. Google Cloud Datastore vs. jBASE vs. Stardog

System Properties Comparison Datomic vs. Google Cloud Datastore vs. jBASE vs. Stardog

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonjBASE  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformA robust multi-value DBMS comprising development tools and middlewareEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelRelational DBMSDocument storeMultivalue DBMSGraph DBMS
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score1.41
Rank#159  Overall
#3  Multivalue DBMS
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Websitewww.datomic.comcloud.google.com/­datastorewww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-jbasewww.stardog.com
Technical documentationdocs.datomic.comcloud.google.com/­datastore/­docsdocs.rocketsoftware.com/­bundle?labelkey=jbase_5.9docs.stardog.com
DeveloperCognitectGoogleRocket Software (formerly Zumasys)Stardog-Union
Initial release2012200819912010
Current release1.0.6735, June 20235.77.3.0, May 2020
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercialcommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, ClojureJava
Server operating systemsAll OS with a Java VMhostedAIX
Linux
Windows
Linux
macOS
Windows
Data schemeyesschema-freeschema-freeschema-free and OWL/RDFS-schema support
Typing infopredefined data types such as float or dateyesyes, details hereoptionalyes
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.nonoyesno infoImport/export of XML data possible
Secondary indexesyesyesyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLnoSQL-like query language (GQL)Embedded SQL for jBASE in BASICYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API
GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
Supported programming languagesClojure
Java
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Basic
Jabbascript
Java
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresyes infoTransaction Functionsusing Google App Engineyesuser defined functions and aggregates, HTTP Server extensions in Java
TriggersBy using transaction functionsCallbacks using the Google Apps Engineyesyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersMulti-source replication using PaxosyesMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency in HA-Cluster
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnoyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDACID
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 developmentnoyesyes
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights can be defined down to the item levelAccess rights for users and roles

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More resources
DatomicGoogle Cloud DatastorejBASEStardog
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