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DBMS > Datomic vs. Google Cloud Bigtable vs. Hive vs. Riak KV

System Properties Comparison Datomic vs. Google Cloud Bigtable vs. Hive vs. Riak KV

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Editorial information provided by DB-Engines
NameDatomic  Xexclude from comparisonGoogle Cloud Bigtable  Xexclude from comparisonHive  Xexclude from comparisonRiak KV  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.data warehouse software for querying and managing large distributed datasets, built on HadoopDistributed, fault tolerant key-value store
Primary database modelRelational DBMSKey-value store
Wide column store
Relational DBMSKey-value store infowith links between data sets and object tags for the creation of secondary indexes
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.59
Rank#150  Overall
#69  Relational DBMS
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score61.17
Rank#18  Overall
#12  Relational DBMS
Score4.10
Rank#82  Overall
#9  Key-value stores
Websitewww.datomic.comcloud.google.com/­bigtablehive.apache.org
Technical documentationdocs.datomic.comcloud.google.com/­bigtable/­docscwiki.apache.org/­confluence/­display/­Hive/­Homewww.tiot.jp/­riak-docs/­riak/­kv/­latest
DeveloperCognitectGoogleApache Software Foundation infoinitially developed by FacebookOpenSource, formerly Basho Technologies
Initial release2012201520122009
Current release1.0.6735, June 20233.1.3, April 20223.2.0, December 2022
License infoCommercial or Open Sourcecommercial infolimited edition freecommercialOpen Source infoApache Version 2Open Source infoApache version 2, commercial enterprise edition
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, ClojureJavaErlang
Server operating systemsAll OS with a Java VMhostedAll OS with a Java VMLinux
OS X
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesnoyesno
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 indexesyesnoyesrestricted
SQL infoSupport of SQLnonoSQL-like DML and DDL statementsno
APIs and other access methodsRESTful HTTP APIgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
JDBC
ODBC
Thrift
HTTP API
Native Erlang Interface
Supported programming languagesClojure
Java
C#
C++
Go
Java
JavaScript (Node.js)
Python
C++
Java
PHP
Python
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresyes infoTransaction Functionsnoyes infouser defined functions and integration of map-reduceErlang
TriggersBy using transaction functionsnonoyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodesnone infoBut extensive use of caching in the application peersShardingShardingSharding infono "single point of failure"
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 zonesselectable replication factorselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyes infoquery execution via MapReduceyes
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual ConsistencyEventual Consistency
Foreign keys infoReferential integritynononono infolinks between data sets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDAtomic single-row operationsnono
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyes infousing external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)yesyesyes
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 developmentno
User concepts infoAccess controlnoAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users, groups and rolesyes, using Riak Security

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DatomicGoogle Cloud BigtableHiveRiak KV
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