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DBMS > Amazon Neptune vs. Badger vs. Google Cloud Spanner vs. Kinetica vs. RDF4J

System Properties Comparison Amazon Neptune vs. Badger vs. Google Cloud Spanner vs. Kinetica vs. RDF4J

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonBadger  Xexclude from comparisonGoogle Cloud Spanner  Xexclude from comparisonKinetica  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.A horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.Fully vectorized database across both GPUs and CPUsRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.
Primary database modelGraph DBMS
RDF store
Key-value storeRelational DBMSRelational DBMSRDF store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.20
Rank#119  Overall
#9  Graph DBMS
#5  RDF stores
Score0.14
Rank#331  Overall
#49  Key-value stores
Score2.89
Rank#103  Overall
#52  Relational DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score0.69
Rank#230  Overall
#9  RDF stores
Websiteaws.amazon.com/­neptunegithub.com/­dgraph-io/­badgercloud.google.com/­spannerwww.kinetica.comrdf4j.org
Technical documentationaws.amazon.com/­neptune/­developer-resourcesgodoc.org/­github.com/­dgraph-io/­badgercloud.google.com/­spanner/­docsdocs.kinetica.comrdf4j.org/­documentation
DeveloperAmazonDGraph LabsGoogleKineticaSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.
Initial release20172017201720122004
Current release7.1, August 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0commercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.
Cloud-based only infoOnly available as a cloud serviceyesnoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoC, C++Java
Server operating systemshostedBSD
Linux
OS X
Solaris
Windows
hostedLinuxLinux
OS X
Unix
Windows
Data schemeschema-freeschema-freeyesyesyes infoRDF Schemas
Typing infopredefined data types such as float or dateyesnoyesyesyes
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.nononono
Secondary indexesnonoyesyesyes
SQL infoSupport of SQLnonoyes infoQuery statements complying to ANSI 2011SQL-like DML and DDL statementsno
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
gRPC (using protocol buffers) API
JDBC infoAt present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
GoGo
Java
JavaScript (Node.js)
Python
C++
Java
JavaScript (Node.js)
Python
Java
PHP
Python
Server-side scripts infoStored proceduresnononouser defined functionsyes
Triggersnononoyes infotriggers when inserted values for one or more columns fall within a specified rangeyes
Partitioning methods infoMethods for storing different data on different nodesnonenoneShardingShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.noneMulti-source replication with 3 replicas for regional instances.Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencynoneImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infoRelationships in graphsnoyes infoby using interleaved tables, this features focuses more on performance improvements than on referential integrityyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACID infoStrict serializable isolationnoACID infoIsolation support depends on the API used
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesyes infoin-memory storage is supported as well
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)noAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table levelno

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More resources
Amazon NeptuneBadgerGoogle Cloud SpannerKineticaRDF4J infoformerly known as Sesame
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