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DBMS > Amazon Neptune vs. Badger vs. BaseX vs. Spark SQL

System Properties Comparison Amazon Neptune vs. Badger vs. BaseX vs. Spark SQL

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Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonBadger  Xexclude from comparisonBaseX  Xexclude from comparisonSpark SQL  Xexclude from comparison
DescriptionFast, reliable graph database built for the cloudAn embeddable, persistent, simple and fast Key-Value Store, written purely in Go.Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.Spark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelGraph DBMS
RDF store
Key-value storeNative XML DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score2.29
Rank#113  Overall
#9  Graph DBMS
#5  RDF stores
Score0.22
Rank#320  Overall
#47  Key-value stores
Score1.84
Rank#135  Overall
#4  Native XML DBMS
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteaws.amazon.com/­neptunegithub.com/­dgraph-io/­badgerbasex.orgspark.apache.org/­sql
Technical documentationaws.amazon.com/­neptune/­developer-resourcesgodoc.org/­github.com/­dgraph-io/­badgerdocs.basex.orgspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonDGraph LabsBaseX GmbHApache Software Foundation
Initial release2017201720072014
Current release11.0, June 20243.5.0 ( 2.13), September 2023
License infoCommercial or Open SourcecommercialOpen Source infoApache 2.0Open Source infoBSD licenseOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageGoJavaScala
Server operating systemshostedBSD
Linux
OS X
Solaris
Windows
Linux
OS X
Windows
Linux
OS X
Windows
Data schemeschema-freeschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesnono infoXQuery supports typesyes
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 indexesnonoyesno
SQL infoSupport of SQLnononoSQL-like DML and DDL statements
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ
JDBC
ODBC
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
GoActionscript
C
C#
Haskell
Java
JavaScript infoNode.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic
Java
Python
R
Scala
Server-side scripts infoStored proceduresnonoyesno
Triggersnonoyes infovia eventsno
Partitioning methods infoMethods for storing different data on different nodesnonenonenoneyes, utilizing Spark Core
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.nonenonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistencynone
Foreign keys infoReferential integrityyes infoRelationships in graphsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnomultiple readers, single writerno
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.nono
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)noUsers with fine-grained authorization concept on 4 levelsno

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More resources
Amazon NeptuneBadgerBaseXSpark SQL
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