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DBMS > GridGain vs. Hive vs. Microsoft Azure Table Storage vs. Teradata Aster vs. TinkerGraph

System Properties Comparison GridGain vs. Hive vs. Microsoft Azure Table Storage vs. Teradata Aster vs. TinkerGraph

Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonHive  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonTeradata Aster  Xexclude from comparisonTinkerGraph  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.
DescriptionGridGain is an in-memory computing platform, built on Apache Ignitedata warehouse software for querying and managing large distributed datasets, built on HadoopA Wide Column Store for rapid development using massive semi-structured datasetsPlatform for big data analytics on multistructured data sources and typesA lightweight, in-memory graph engine that serves as a reference implementation of the TinkerPop3 API
Primary database modelKey-value store
Relational DBMS
Relational DBMSWide column storeRelational DBMSGraph DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.53
Rank#155  Overall
#26  Key-value stores
#73  Relational DBMS
Score62.59
Rank#18  Overall
#12  Relational DBMS
Score4.92
Rank#73  Overall
#6  Wide column stores
Score0.12
Rank#344  Overall
#34  Graph DBMS
Websitewww.gridgain.comhive.apache.orgazure.microsoft.com/­en-us/­services/­storage/­tablestinkerpop.apache.org/­docs/­current/­reference/­#tinkergraph-gremlin
Technical documentationwww.gridgain.com/­docs/­index.htmlcwiki.apache.org/­confluence/­display/­Hive/­Home
DeveloperGridGain Systems, Inc.Apache Software Foundation infoinitially developed by FacebookMicrosoftTeradata
Initial release20072012201220052009
Current releaseGridGain 8.5.13.1.3, April 2022
License infoCommercial or Open SourcecommercialOpen Source infoApache Version 2commercialcommercialOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .NetJavaJava
Server operating systemsLinux
OS X
Solaris
Windows
All OS with a Java VMhostedLinux
Data schemeyesyesschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Storeschema-free
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.yesnoyes infoin Aster File Storeno
Secondary indexesyesyesnoyesno
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsnoyesno
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
Thrift
RESTful HTTP APIADO.NET
JDBC
ODBC
OLE DB
TinkerPop 3
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
PHP
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
Python
R
Groovy
Java
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)yes infouser defined functions and integration of map-reducenoR packagesno
Triggersyes (cache interceptors and events)nononono
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceShardingnone
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)selectable replication factoryes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)yes infoquery execution via MapReducenoyes infoSQL Map-Reduce Frameworkno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationnone
Foreign keys infoReferential integritynonononoyes infoRelationships in graphs
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnooptimistic lockingACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesno
Durability infoSupport for making data persistentyesyesyesyesoptional
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonoyes
User concepts infoAccess controlSecurity Hooks for custom implementationsAccess rights for users, groups and rolesAccess rights based on private key authentication or shared access signaturesfine grained access rights according to SQL-standardno

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More resources
GridGainHiveMicrosoft Azure Table StorageTeradata AsterTinkerGraph
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