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DBMS > Amazon Redshift vs. Datomic vs. RethinkDB vs. Splice Machine

System Properties Comparison Amazon Redshift vs. Datomic vs. RethinkDB vs. Splice Machine

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Editorial information provided by DB-Engines
NameAmazon Redshift  Xexclude from comparisonDatomic  Xexclude from comparisonRethinkDB  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionLarge scale data warehouse service for use with business intelligence toolsDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityDBMS for the Web with a mechanism to push updated query results to applications in realtime.Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMSDocument storeRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score22.86
Rank#33  Overall
#19  Relational DBMS
Score1.86
Rank#124  Overall
#61  Relational DBMS
Score3.19
Rank#94  Overall
#17  Document stores
Score0.71
Rank#206  Overall
#102  Relational DBMS
Websiteaws.amazon.com/­redshiftwww.datomic.comrethinkdb.comsplicemachine.com
Technical documentationdocs.aws.amazon.com/­redshiftdocs.datomic.comrethinkdb.com/­docsdoc.splicemachine.com
DeveloperAmazon (based on PostgreSQL)CognitectThe Linux Foundation infosince July 2017Splice Machine
Initial release2012201220092014
Current release0.9.5951, July 20192.4.1, August 20203.1, March 2021
License infoCommercial or Open Sourcecommercialcommercial infolimited edition freeOpen Source infoApache Version 2Open Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageCJava, ClojureC++Java
Server operating systemshostedAll OS with a Java VMLinux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes infostring, binary, float, bool, date, geometryyes
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 indexesrestrictedyesyesyes
SQL infoSupport of SQLyes infodoes not fully support an SQL-standardnonoyes
APIs and other access methodsJDBC
ODBC
RESTful HTTP APIJDBC
Native Spark Datasource
ODBC
Supported programming languagesAll languages supporting JDBC/ODBCClojure
Java
C infocommunity-supported driver
C# infocommunity-supported driver
C++ infocommunity-supported driver
Clojure infocommunity-supported driver
Dart infocommunity-supported driver
Erlang infocommunity-supported driver
Go infocommunity-supported driver
Haskell infocommunity-supported driver
Java infoofficial driver
JavaScript (Node.js) infoofficial driver
Lisp infocommunity-supported driver
Lua infocommunity-supported driver
Objective-C infocommunity-supported driver
Perl infocommunity-supported driver
PHP infocommunity-supported driver
Python infoofficial driver
Ruby infoofficial driver
Scala infocommunity-supported driver
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresuser defined functions infoin Pythonyes infoTransaction Functionsyes infoJava
TriggersnoBy using transaction functionsClient-side triggers through changefeedsyes
Partitioning methods infoMethods for storing different data on different nodesShardingnone infoBut extensive use of caching in the application peersSharding inforange basedShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesyesnone infoBut extensive use of caching in the application peersSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnonoyesYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyes infoinformational only, not enforced by the systemnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDACIDAtomic single-document operationsACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes infoMVCC basedyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes inforecommended only for testing and developmentnoyes
User concepts infoAccess controlfine grained access rights according to SQL-standardnoyes infousers and table-level permissionsAccess rights for users, groups and roles according to SQL-standard
More information provided by the system vendor
Amazon RedshiftDatomicRethinkDBSplice Machine
News

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11 June 2021

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1 June 2021

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23 April 2021

How Optimizing MLOps Can Revolutionize Enterprise AI
15 April 2021

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More resources
Amazon RedshiftDatomicRethinkDBSplice Machine
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