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DBMS > Amazon DocumentDB vs. IBM Db2 warehouse vs. Splice Machine

System Properties Comparison Amazon DocumentDB vs. IBM Db2 warehouse vs. Splice Machine

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Editorial information provided by DB-Engines
NameAmazon DocumentDB  Xexclude from comparisonIBM Db2 warehouse infoformerly named IBM dashDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionFast, scalable, highly available, and fully managed MongoDB-compatible database serviceCloud-based data warehousing serviceOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelDocument storeRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.91
Rank#132  Overall
#24  Document stores
Score1.30
Rank#164  Overall
#75  Relational DBMS
Score0.54
Rank#250  Overall
#114  Relational DBMS
Websiteaws.amazon.com/­documentdbwww.ibm.com/­products/­db2/­warehousesplicemachine.com
Technical documentationaws.amazon.com/­documentdb/­resourcessplicemachine.com/­how-it-works
DeveloperIBMSplice Machine
Initial release201920142014
Current release3.1, March 2021
License infoCommercial or Open SourcecommercialcommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava
Server operating systemshostedhostedLinux
OS X
Solaris
Windows
Data schemeschema-freeyesyes
Typing infopredefined data types such as float or dateyesyesyes
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.nono infoImport/export of XML data possible
Secondary indexesyesyesyes
SQL infoSupport of SQLnoyesyes
APIs and other access methodsproprietary protocol using JSON (MongoDB compatible).NET Client API
JDBC
ODBC
OLE DB
JDBC
Native Spark Datasource
ODBC
Supported programming languagesGo
Java
JavaScript (Node.js)
PHP
Python
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresnoPL/SQL, SQL PLyes infoJava
Triggersnoyesyes
Partitioning methods infoMethods for storing different data on different nodesnoneShardingShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-availability zones for high availability, asynchronous replication for up to 15 read replicasyesMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsno infomay be implemented via Amazon Elastic MapReduce (Amazon EMR)noYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityno infotypically not used, however similar functionality with DBRef possibleyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-document operationsACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes
User concepts infoAccess controlAccess rights for users and rolesfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon DocumentDBIBM Db2 warehouse infoformerly named IBM dashDBSplice Machine
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