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DBMS > GridGain vs. Kinetica vs. Microsoft Azure Table Storage

System Properties Comparison GridGain vs. Kinetica vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameGridGain  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparison
DescriptionGridGain is an in-memory computing platform, built on Apache IgniteFully vectorized database across both GPUs and CPUsA Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelColumnar
Key-value store
Object oriented DBMS
Relational DBMS
Relational DBMSWide column store
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.37
Rank#154  Overall
#1  Columnar
#26  Key-value stores
#2  Object oriented DBMS
#70  Relational DBMS
Score0.45
Rank#254  Overall
#118  Relational DBMS
Score3.05
Rank#84  Overall
#6  Wide column stores
Websitewww.gridgain.comwww.kinetica.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationwww.gridgain.com/­docs/­index.htmldocs.kinetica.com
DeveloperGridGain Systems, Inc.KineticaMicrosoft
Initial release200720122012
Current releaseGridGain 8.5.17.1, August 2021
License infoCommercial or Open Sourcecommercial, open sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJava, C++, .Net, Python, REST, SQLC, C++
Server operating systemsLinux
OS X
Solaris
Windows
z/OS
Linuxhosted
Data schemeyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyes
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.yesnono
Secondary indexesyesyesno
SQL infoSupport of SQLANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsno
APIs and other access methodsHDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
RESTful HTTP API
RESTful HTTP API
Supported programming languagesC#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes (compute grid and cache interceptors can be used instead)user defined functionsno
Triggersyes (cache interceptors and events)yes infotriggers when inserted values for one or more columns fall within a specified rangeno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes (replicated cache)Source-replica replicationyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes (compute grid and hadoop accelerator)nono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integritynoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnooptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlRole-based access control
Security Hooks for custom implementations
Access rights for users and roles on table levelAccess rights based on private key authentication or shared access signatures

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More resources
GridGainKineticaMicrosoft Azure Table Storage
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