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DBMS > Amazon Aurora vs. Apache Impala vs. Datomic vs. Interbase vs. Splice Machine

System Properties Comparison Amazon Aurora vs. Apache Impala vs. Datomic vs. Interbase vs. Splice Machine

Editorial information provided by DB-Engines
NameAmazon Aurora  Xexclude from comparisonApache Impala  Xexclude from comparisonDatomic  Xexclude from comparisonInterbase  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionMySQL and PostgreSQL compatible cloud service by AmazonAnalytic DBMS for HadoopDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityLight-weight proven RDBMS infooriginally from BorlandOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSRelational DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument storeDocument store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score7.57
Rank#51  Overall
#32  Relational DBMS
Score12.45
Rank#40  Overall
#24  Relational DBMS
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score4.08
Rank#75  Overall
#41  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websiteaws.amazon.com/­rds/­auroraimpala.apache.orgwww.datomic.comwww.embarcadero.com/­products/­interbasesplicemachine.com
Technical documentationdocs.aws.amazon.com/­AmazonRDS/­latest/­AuroraUserGuide/­CHAP_Aurora.htmlimpala.apache.org/­impala-docs.htmldocs.datomic.comdocs.embarcadero.com/­products/­interbasesplicemachine.com/­how-it-works
DeveloperAmazonApache Software Foundation infoApache top-level project, originally developed by ClouderaCognitectEmbarcaderoSplice Machine
Initial release20152013201219842014
Current release4.1.0, June 20221.0.7075, December 2023InterBase 2020, December 20193.1, March 2021
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercial infolimited edition freecommercialOpen Source infoAGPL 3.0, 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, ClojureCJava
Server operating systemshostedLinuxAll OS with a Java VMAndroid
iOS
Linux
OS X
Windows
Linux
OS X
Solaris
Windows
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.yesnonono infoexport as XML data possible
Secondary indexesyesyesyesyesyes
SQL infoSupport of SQLyesSQL-like DML and DDL statementsnoyesyes
APIs and other access methodsADO.NET
JDBC
ODBC
JDBC
ODBC
RESTful HTTP APIADO.NET
JDBC
ODBC
JDBC
Native Spark Datasource
ODBC
Supported programming languagesAda
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
All languages supporting JDBC/ODBCClojure
Java
.Net
C
C++
Delphi
Java
Object Pascal
PHP
Ruby
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyesyes infouser defined functions and integration of map-reduceyes infoTransaction Functionsyes infoInterbase procedure and trigger languageyes infoJava
TriggersyesnoBy using transaction functionsyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioningShardingnone infoBut extensive use of caching in the application peersnoneShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationselectable replication factornone infoBut extensive use of caching in the application peersInterbase Change ViewsMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyes infoquery execution via MapReducenonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate ConsistencyImmediate Consistency
Foreign keys infoReferential integrityyesnonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnoACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoMultiversion concurreny controlyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyesyesyes 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.yesnoyes inforecommended only for testing and developmentyesyes
User concepts infoAccess controlfine grained access rights according to SQL-standardAccess rights for users, groups and roles infobased on Apache Sentry and Kerberosnofine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
Amazon AuroraApache ImpalaDatomicInterbaseSplice Machine
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