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DBMS > Datomic vs. Google Cloud Bigtable vs. jBASE vs. Linter vs. Splice Machine

System Properties Comparison Datomic vs. Google Cloud Bigtable vs. jBASE vs. Linter vs. Splice Machine

Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonjBASE  Xexclude from comparisonLinter  Xexclude from comparisonSplice Machine  Xexclude from comparison
DescriptionDatomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durabilityGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.A robust multi-value DBMS comprising development tools and middlewareRDBMS for high security requirementsOpen-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark
Primary database modelRelational DBMSKey-value store
Wide column store
Multivalue DBMSRelational DBMSRelational DBMS
Secondary database modelsSpatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.66
Rank#144  Overall
#66  Relational DBMS
Score3.15
Rank#95  Overall
#14  Key-value stores
#8  Wide column stores
Score1.49
Rank#156  Overall
#3  Multivalue DBMS
Score0.12
Rank#350  Overall
#152  Relational DBMS
Score0.54
Rank#252  Overall
#115  Relational DBMS
Websitewww.datomic.comcloud.google.com/­bigtablewww.rocketsoftware.com/­products/­rocket-multivalue-application-development-platform/­rocket-jbaselinter.rusplicemachine.com
Technical documentationdocs.datomic.comcloud.google.com/­bigtable/­docsdocs.rocketsoftware.com/­bundle?labelkey=jbase_5.9splicemachine.com/­how-it-works
DeveloperCognitectGoogleRocket Software (formerly Zumasys)relex.ruSplice Machine
Initial release20122015199119902014
Current release1.0.6735, June 20235.73.1, March 2021
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialcommercialcommercialOpen Source infoAGPL 3.0, commercial license available
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureC and C++Java
Server operating systemsAll OS with a Java VMhostedAIX
Linux
Windows
AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows
Linux
OS X
Solaris
Windows
Data schemeyesschema-freeschema-freeyesyes
Typing infopredefined data types such as float or dateyesnooptionalyesyes
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.nonoyesno
Secondary indexesyesnoyesyes
SQL infoSupport of SQLnonoEmbedded SQL for jBASE in BASICyesyes
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
SOAP-based API
ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)
JDBC
Native Spark Datasource
ODBC
Supported programming languagesClojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Basic
Jabbascript
Java
C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl
C#
C++
Java
JavaScript (Node.js)
Python
R
Scala
Server-side scripts infoStored proceduresyes infoTransaction Functionsnoyesyes infoproprietary syntax with the possibility to convert from PL/SQLyes infoJava
TriggersBy using transaction functionsnoyesyesyes
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingnoneShared Nothhing Auto-Sharding, Columnar Partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesnone infoBut extensive use of caching in the application peersInternal replication in Colossus, and regional replication between two clusters in different zonesyesSource-replica replicationMulti-source replication
Source-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesnonoYes, via Full Spark Integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynononoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsACIDACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, multi-version concurrency control (MVCC)
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes inforecommended only for testing and developmentnoyesyes
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights can be defined down to the item levelfine grained access rights according to SQL-standardAccess rights for users, groups and roles according to SQL-standard

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More resources
DatomicGoogle Cloud BigtablejBASELinterSplice Machine
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