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DBMS > Google Cloud Datastore vs. GridGain vs. Kinetica vs. Quasardb

System Properties Comparison Google Cloud Datastore vs. GridGain vs. Kinetica vs. Quasardb

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Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonGridGain  Xexclude from comparisonKinetica  Xexclude from comparisonQuasardb  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformGridGain is an in-memory computing platform, built on Apache IgniteFully vectorized database across both GPUs and CPUsDistributed, high-performance timeseries database
Primary database modelDocument storeColumnar
Key-value store
Object oriented DBMS
Relational DBMS
Relational DBMSTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.13
Rank#71  Overall
#12  Document stores
Score1.48
Rank#150  Overall
#1  Columnar
#26  Key-value stores
#2  Object oriented DBMS
#69  Relational DBMS
Score0.42
Rank#261  Overall
#120  Relational DBMS
Score0.13
Rank#332  Overall
#28  Time Series DBMS
Websitecloud.google.com/­datastorewww.gridgain.comwww.kinetica.comquasar.ai
Technical documentationcloud.google.com/­datastore/­docswww.gridgain.com/­docs/­index.htmldocs.kinetica.comdoc.quasar.ai/­master
DeveloperGoogleGridGain Systems, Inc.Kineticaquasardb
Initial release2008200720122009
Current releaseGridGain 8.5.17.1, August 20213.14.1, January 2024
License infoCommercial or Open Sourcecommercialcommercial, open sourcecommercialcommercial infoFree community edition, Non-profit organizations and non-commercial usage are eligible for free licenses
Cloud-based only infoOnly available as a cloud serviceyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJava, C++, .Net, Python, REST, SQLC, C++C++
Server operating systemshostedLinux
OS X
Solaris
Windows
z/OS
LinuxBSD
Linux
OS X
Windows
Data schemeschema-freeyesyesschema-free
Typing infopredefined data types such as float or dateyes, details hereyesyesyes infointeger and binary
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.noyesnono
Secondary indexesyesyesyesyes infowith tags
SQL infoSupport of SQLSQL-like query language (GQL)ANSI-99 for query and DML statements, subset of DDLSQL-like DML and DDL statementsSQL-like query language
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
JDBC
ODBC
RESTful HTTP API
HTTP API
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C#
C++
Java
PHP
Python
Ruby
Scala
C++
Java
JavaScript (Node.js)
Python
.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R
Server-side scripts infoStored proceduresusing Google App Engineyes (compute grid and cache interceptors can be used instead)user defined functionsno
TriggersCallbacks using the Google Apps Engineyes (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 nodesShardingShardingShardingSharding infoconsistent hashing
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using Paxosyes (replicated cache)Source-replica replicationSource-replica replication with selectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflowyes (compute grid and hadoop accelerator)nowith Hadoop integration
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACIDnoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoby using LevelDB
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoGPU vRAM or System RAMyes infoTransient mode
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Role-based access control
Security Hooks for custom implementations
Access rights for users and roles on table levelCryptographically strong user authentication and audit trail

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More resources
Google Cloud DatastoreGridGainKineticaQuasardb
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