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DBMS > Apache Impala vs. Google Cloud Datastore vs. Kinetica vs. Microsoft Azure SQL Database vs. VictoriaMetrics

System Properties Comparison Apache Impala vs. Google Cloud Datastore vs. Kinetica vs. Microsoft Azure SQL Database vs. VictoriaMetrics

Editorial information provided by DB-Engines
NameApache Impala  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonKinetica  Xexclude from comparisonMicrosoft Azure SQL Database infoformerly SQL Azure  Xexclude from comparisonVictoriaMetrics  Xexclude from comparison
DescriptionAnalytic DBMS for HadoopAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully 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 modelRelational DBMSDocument storeRelational DBMSRelational DBMSTime Series DBMS
Secondary database modelsDocument storeSpatial DBMS
Time Series DBMS
Document store
Graph DBMS
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score13.77
Rank#40  Overall
#24  Relational DBMS
Score4.47
Rank#76  Overall
#12  Document stores
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
Websiteimpala.apache.orgcloud.google.com/­datastorewww.kinetica.comazure.microsoft.com/­en-us/­products/­azure-sql/­databasevictoriametrics.com
Technical documentationimpala.apache.org/­impala-docs.htmlcloud.google.com/­datastore/­docsdocs.kinetica.comdocs.microsoft.com/­en-us/­azure/­azure-sqldocs.victoriametrics.com
github.com/­VictoriaMetrics/­VictoriaMetrics/­wiki
DeveloperApache Software Foundation infoApache top-level project, originally developed by ClouderaGoogleKineticaMicrosoftVictoriaMetrics
Initial release20132008201220102018
Current release4.1.0, June 20227.1, August 2021V12v1.91, May 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2commercialcommercialcommercialOpen Source infoApache Version 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC++C, C++C++Go
Server operating systemsLinuxhostedLinuxhostedFreeBSD
Linux
macOS
OpenBSD
Data schemeyesschema-freeyesyes
Typing infopredefined data types such as float or dateyesyes, details hereyesyes
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 indexesyesyesyesyes
SQL infoSupport of SQLSQL-like DML and DDL statementsSQL-like query language (GQL)SQL-like DML and DDL statementsyesno
APIs and other access methodsJDBC
ODBC
gRPC (using protocol buffers) API
RESTful HTTP/JSON 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 languagesAll languages supporting JDBC/ODBC.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyes infouser defined functions and integration of map-reduceusing Google App Engineuser defined functionsTransact SQLno
TriggersnoCallbacks using the Google Apps Engineyes 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 nodesselectable replication factorMulti-source replication using PaxosSource-replica replicationyes, with always 3 replicas availableSynchronous replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsyes infoquery execution via MapReduceyes infousing Google Cloud Dataflownonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual ConsistencyImmediate 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 Consistency or Eventual Consistency depending on configurationImmediate ConsistencyEventual Consistency
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnoACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nonoyes infoGPU vRAM or System RAMno
User concepts infoAccess controlAccess rights for users, groups and roles infobased on Apache Sentry and KerberosAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Access rights for users and roles on table levelfine grained access rights according to SQL-standard

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More resources
Apache ImpalaGoogle Cloud DatastoreKineticaMicrosoft Azure SQL Database infoformerly SQL AzureVictoriaMetrics
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