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DBMS > Amazon Neptune vs. Microsoft Azure Table Storage vs. Spark SQL vs. Stardog

System Properties Comparison Amazon Neptune vs. Microsoft Azure Table Storage vs. Spark SQL vs. Stardog

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonSpark SQL  Xexclude from comparisonStardog  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudA Wide Column Store for rapid development using massive semi-structured datasetsSpark SQL is a component on top of 'Spark Core' for structured data processingEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization
Primary database modelGraph DBMS
RDF store
Wide column storeRelational DBMSGraph DBMS
RDF store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score4.04
Rank#77  Overall
#6  Wide column stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Score2.07
Rank#122  Overall
#11  Graph DBMS
#6  RDF stores
Websiteaws.amazon.com/­neptuneazure.microsoft.com/­en-us/­services/­storage/­tablesspark.apache.org/­sqlwww.stardog.com
Technical documentationaws.amazon.com/­neptune/­developer-resourcesspark.apache.org/­docs/­latest/­sql-programming-guide.htmldocs.stardog.com
DeveloperAmazonMicrosoftApache Software FoundationStardog-Union
Initial release2017201220142010
Current release3.5.0 ( 2.13), September 20237.3.0, May 2020
License infoCommercial or Open SourcecommercialcommercialOpen Source infoApache 2.0commercial 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 serviceyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageScalaJava
Server operating systemshostedhostedLinux
OS X
Windows
Linux
macOS
Windows
Data schemeschema-freeschema-freeyesschema-free and OWL/RDFS-schema support
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.nononono infoImport/export of XML data possible
Secondary indexesnononoyes infosupports real-time indexing in full-text and geospatial
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsYes, compatible with all major SQL variants through dedicated BI/SQL Server
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
RESTful HTTP APIJDBC
ODBC
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 languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresnononouser defined functions and aggregates, HTTP Server extensions in Java
Triggersnononoyes infovia event handlers
Partitioning methods infoMethods for storing different data on different nodesnoneSharding infoImplicit feature of the cloud serviceyes, utilizing Spark Corenone
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.yes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.noneMulti-source replication in HA-Cluster
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency in HA-Cluster
Foreign keys infoReferential integrityyes infoRelationships in graphsnonoyes inforelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDoptimistic lockingnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)Access rights based on private key authentication or shared access signaturesnoAccess rights for users and roles

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More resources
Amazon NeptuneMicrosoft Azure Table StorageSpark SQLStardog
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