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DBMS > Google Cloud Datastore vs. IBM Db2 Event Store vs. Kinetica vs. Microsoft Azure SQL Database vs. VictoriaMetrics

System Properties Comparison Google Cloud Datastore vs. IBM Db2 Event Store vs. Kinetica vs. Microsoft Azure SQL Database vs. VictoriaMetrics

Editorial information provided by DB-Engines
NameGoogle Cloud Datastore  Xexclude from comparisonIBM Db2 Event Store  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformDistributed Event Store optimized for Internet of Things use casesFully vectorized database across both GPUs and CPUsDatabase as a Service offering with high compatibility to Microsoft SQL ServerA fast, cost-effective and scalable Time Series DBMS and monitoring solution
Primary database modelDocument storeEvent Store
Time Series DBMS
Relational DBMSRelational DBMSTime Series DBMS
Secondary database modelsSpatial DBMS
Time Series DBMS
Document store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score4.47
Rank#76  Overall
#12  Document stores
Score0.19
Rank#323  Overall
#2  Event Stores
#28  Time Series DBMS
Score0.64
Rank#236  Overall
#109  Relational DBMS
Score77.99
Rank#16  Overall
#11  Relational DBMS
Score1.32
Rank#162  Overall
#14  Time Series DBMS
Websitecloud.google.com/­datastorewww.ibm.com/­products/­db2-event-storewww.kinetica.comazure.microsoft.com/­en-us/­products/­azure-sql/­databasevictoriametrics.com
Technical documentationcloud.google.com/­datastore/­docswww.ibm.com/­docs/­en/­db2-event-storedocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­azure-sqldocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperGoogleIBMKineticaMicrosoftVictoriaMetrics
Initial release20082017201220102018
Current release2.07.1, August 2021V12v1.91, May 2023
License infoCommercial or Open Sourcecommercialcommercial infofree developer edition availablecommercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud serviceyesnonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C, C++C++Go
Server operating systemshostedLinux infoLinux, macOS, Windows for the developer additionLinuxhostedFreeBSD
Linux
macOS
OpenBSD
Data schemeschema-freeyesyesyes
Typing infopredefined data types such as float or dateyes, details hereyesyesyes
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.nononoyesno
Secondary indexesyesnoyesyes
SQL infoSupport of SQLSQL-like query language (GQL)yes infothrough the embedded Spark runtimeSQL-like DML and DDL statementsyesno
APIs and other access methodsgRPC (using protocol buffers) API
RESTful HTTP/JSON API
ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API
JDBC
ODBC
RESTful HTTP API
ADO.NET
JDBC
ODBC
Graphite protocol
InfluxDB Line Protocol
OpenTSDB
Prometheus Query API
Prometheus Remote Read/Write
Supported programming languages.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic
C++
Java
JavaScript (Node.js)
Python
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresusing Google App Engineyesuser defined functionsTransact SQLno
TriggersCallbacks using the Google Apps Enginenoyes infotriggers when inserted values for one or more columns fall within a specified rangeyesno
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMulti-source replication using PaxosActive-active shard replicationSource-replica replicationyes, with always 3 replicas availableSynchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infousing Google Cloud Dataflownononono
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.Eventual ConsistencyImmediate Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integrityyes infovia ReferenceProperties or Ancestor pathsnoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnonoACIDno
Concurrency infoSupport for concurrent manipulation of datayesNo - written data is immutableyesyesyes
Durability infoSupport for making data persistentyesYes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storageyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyesyes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)fine grained access rights according to SQL-standardAccess rights for users and roles on table levelfine grained access rights according to SQL-standard

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More resources
Google Cloud DatastoreIBM Db2 Event StoreKineticaMicrosoft Azure SQL Database infoformerly SQL AzureVictoriaMetrics
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