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DBMS > Amazon SimpleDB vs. Oracle Berkeley DB vs. Spark SQL vs. Transbase vs. WakandaDB

System Properties Comparison Amazon SimpleDB vs. Oracle Berkeley DB vs. Spark SQL vs. Transbase vs. WakandaDB

Editorial information provided by DB-Engines
NameAmazon SimpleDB  Xexclude from comparisonOracle Berkeley DB  Xexclude from comparisonSpark SQL  Xexclude from comparisonTransbase  Xexclude from comparisonWakandaDB  Xexclude from comparison
DescriptionHosted simple database service by Amazon, with the data stored in the Amazon Cloud. infoThere is an unrelated product called SimpleDB developed by Edward ScioreWidely used in-process key-value storeSpark SQL is a component on top of 'Spark Core' for structured data processingA resource-optimized, high-performance, universally applicable RDBMSWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelKey-value storeKey-value store infosupports sorted and unsorted key sets
Native XML DBMS infoin the Oracle Berkeley DB XML version
Relational DBMSRelational DBMSObject oriented DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.85
Rank#138  Overall
#24  Key-value stores
Score2.21
Rank#117  Overall
#20  Key-value stores
#3  Native XML DBMS
Score18.96
Rank#33  Overall
#20  Relational DBMS
Score0.11
Rank#341  Overall
#150  Relational DBMS
Score0.03
Rank#364  Overall
#17  Object oriented DBMS
Websiteaws.amazon.com/­simpledbwww.oracle.com/­database/­technologies/­related/­berkeleydb.htmlspark.apache.org/­sqlwww.transaction.de/­en/­products/­transbase.htmlwakanda.github.io
Technical documentationdocs.aws.amazon.com/­simpledbdocs.oracle.com/­cd/­E17076_05/­html/­index.htmlspark.apache.org/­docs/­latest/­sql-programming-guide.htmlwww.transaction.de/­en/­products/­transbase/­features.htmlwakanda.github.io/­doc
DeveloperAmazonOracle infooriginally developed by Sleepycat, which was acquired by OracleApache Software FoundationTransaction Software GmbHWakanda SAS
Initial release20071994201419872012
Current release18.1.40, May 20203.5.0 ( 2.13), September 2023Transbase 8.3, 20222.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourcecommercialOpen Source infocommercial license availableOpen Source infoApache 2.0commercial infofree development licenseOpen Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnononono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, Java, C++ (depending on the Berkeley DB edition)ScalaC and C++C++, JavaScript
Server operating systemshostedAIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows
Linux
OS X
Windows
FreeBSD
Linux
macOS
Solaris
Windows
Linux
OS X
Windows
Data schemeschema-freeschema-freeyesyesyes
Typing infopredefined data types such as float or datenonoyesyesyes
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.yes infoonly with the Berkeley DB XML editionnonono
Secondary indexesyes infoAll columns are indexed automaticallyyesnoyes
SQL infoSupport of SQLnoyes infoSQL interfaced based on SQLite is availableSQL-like DML and DDL statementsyesno
APIs and other access methodsRESTful HTTP APIJDBC
ODBC
ADO.NET
JDBC
ODBC
Proprietary native API
RESTful HTTP API
Supported programming languages.Net
C
C++
Erlang
Java
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++
Java
JavaScript
Kotlin
Objective-C
PHP
Python
JavaScript
Server-side scripts infoStored proceduresnononoyesyes
Triggersnoyes infoonly for the SQL APInoyesyes
Partitioning methods infoMethods for storing different data on different nodesnone infoSharding must be implemented in the applicationnoneyes, utilizing Spark Corenone
Replication methods infoMethods for redundantly storing data on multiple nodesyesSource-replica replicationnoneSource-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsnononono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Immediate Consistency infocan be specified for read operations
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoConcurrent data updates can be detected by the applicationACIDnoyesACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
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-standardyes

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More resources
Amazon SimpleDBOracle Berkeley DBSpark SQLTransbaseWakandaDB
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