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DBMS > Amazon Neptune vs. Oracle Berkeley DB vs. Spark SQL vs. SWC-DB vs. Teradata Aster

System Properties Comparison Amazon Neptune vs. Oracle Berkeley DB vs. Spark SQL vs. SWC-DB vs. Teradata Aster

Editorial information provided by DB-Engines
NameAmazon Neptune  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparisonSWC-DB infoSuper Wide Column Database  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionFast, reliable graph database built for the cloudWidely used in-process key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processingA high performance, scalable Wide Column DBMSPlatform for big data analytics on multistructured data sources and types
Primary database modelGraph DBMS
RDF store
Key-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSWide column storeRelational DBMS
Secondary database modelsTime 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
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.01
Rank#376  Overall
#13  Wide column stores
Websiteaws.amazon.com/­neptunewww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sqlgithub.com/­kashirin-alex/­swc-db
www.swcdb.org
Technical documentationaws.amazon.com/­neptune/­developer-resourcesdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperAmazonOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software FoundationAlex KashirinTeradata
Initial release20171994201420202005
Current release18.1.40, May 20203.5.0 ( 2.13), September 20230.5, April 2021
License infoCommercial or Open SourcecommercialOpen Source infocommercial license availableOpen Source infoApache 2.0Open Source infoGPL V3commercial
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageC, Java, C++ (depending on the Berkeley DB edition)ScalaC++
Server operating systemshostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
LinuxLinux
Data schemeschema-freeschema-freeyesschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
Typing infopredefined data types such as float or dateyesnoyesyes
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 infoonly with the Berkeley DB XML editionnonoyes infoin Aster File Store
Secondary indexesnoyesnoyes
SQL infoSupport of SQLnoyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statementsSQL-like query languageyes
APIs and other access methodsOpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin
JDBC
ODBC
Proprietary protocol
Thrift
ADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC#
Go
Java
JavaScript
PHP
Python
Ruby
Scala
.Net infoFigaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others infoThird-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js) info3rd party binding
Perl
Python
Tcl
Java
Python
R
Scala
C++C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresnonononoR packages
Triggersnoyes infoonly for the SQL APInonono
Partitioning methods infoMethods for storing different data on different nodesnonenoneyes, utilizing Spark CoreShardingSharding
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.Source-replica replicationnoneyes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononoyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integrityyes infoRelationships in graphsnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infowith encyption-at-restyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlAccess rights for users and roles can be defined via the AWS Identity and Access Management (IAM)nonofine grained access rights according to SQL-standard

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More resources
Amazon NeptuneOracle Berkeley DBSpark SQLSWC-DB infoSuper Wide Column DatabaseTeradata Aster
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