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DBMS > Axibase vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer vs. SiteWhere vs. Tarantool

System Properties Comparison Axibase vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer vs. SiteWhere vs. Tarantool

Editorial information provided by DB-Engines
NameAxibase  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSiteWhere  Xexclude from comparisonTarantool  Xexclude from comparison
DescriptionScalable TimeSeries DBMS based on HBase with integrated rule engine and visualizationAutomatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully managed big data interactive analytics platformM2M integration platform for persisting/querying time series dataIn-memory computing platform with a flexible data schema for efficiently building high-performance applications
Primary database modelTime Series DBMSDocument storeRelational DBMS infocolumn orientedTime Series DBMSDocument store
Key-value store
Relational DBMS
Secondary database modelsDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
Spatial DBMS infowith Tarantool/GIS extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score0.29
Rank#292  Overall
#25  Time Series DBMS
Score4.47
Rank#76  Overall
#12  Document stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score0.06
Rank#356  Overall
#35  Time Series DBMS
Score1.72
Rank#144  Overall
#25  Document stores
#25  Key-value stores
#66  Relational DBMS
Websiteaxibase.com/­docs/­atsd/­financecloud.google.com/­datastoreazure.microsoft.com/­services/­data-explorergithub.com/­sitewhere/­sitewherewww.tarantool.io
Technical documentationcloud.google.com/­datastore/­docsdocs.microsoft.com/­en-us/­azure/­data-explorersitewhere1.sitewhere.io/­index.htmlwww.tarantool.io/­en/­doc
DeveloperAxibase CorporationGoogleMicrosoftSiteWhereVK
Initial release20132008201920102008
Current release15585cloud service with continuous releases2.10.0, May 2022
License infoCommercial or Open Sourcecommercial infoCommunity Edition (single node) is free, Enterprise Edition (distributed) is paidcommercialcommercialOpen Source infoCommon Public Attribution License Version 1.0Open Source infoBSD-2, source-available extensions (modules), commercial licenses for Tarantool Enterprise
Cloud-based only infoOnly available as a cloud servicenoyesyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJavaC and C++
Server operating systemsLinuxhostedhostedLinux
OS X
Windows
BSD
Linux
macOS
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)predefined schemeFlexible data schema: relational definition for tables with ability to store json-like documents in columns
Typing infopredefined data types such as float or dateyes infoshort, integer, long, float, double, decimal, stringyes, details hereyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesstring, double, decimal, uuid, integer, blob, boolean, datetime
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.nonoyesnono
Secondary indexesnoyesall fields are automatically indexednoyes
SQL infoSupport of SQLSQL-like query languageSQL-like query language (GQL)Kusto Query Language (KQL), SQL subsetnoFull-featured ANSI SQL support
APIs and other access methodsJDBC
Proprietary protocol (Network API)
RESTful HTTP API
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
HTTP RESTOpen binary protocol
Supported programming languagesGo
Java
PHP
Python
R
Ruby
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
C
C#
C++
Erlang
Go
Java
JavaScript
Lua
Perl
PHP
Python
Rust
Server-side scripts infoStored proceduresyesusing Google App EngineYes, possible languages: KQL, Python, RLua, C and SQL stored procedures
TriggersyesCallbacks using the Google Apps Engineyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes, before/after data modification events, on replication events, client session events
Partitioning methods infoMethods for storing different data on different nodesShardingShardingSharding infoImplicit feature of the cloud serviceSharding infobased on HBaseSharding, partitioned with virtual buckets by user defined affinity key. Live resharding for scale up and scale down without maintenance downtime.
Replication methods infoMethods for redundantly storing data on multiple nodesSource-replica replicationMulti-source replication using Paxosyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor infobased on HBaseAsynchronous replication with multi-master option
Configurable replication topology (full-mesh, chain, star)
Synchronous quorum replication (with Raft)
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesyes infousing Google Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
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 Consistency
Immediate Consistency
Immediate ConsistencyCasual consistency across sharding partitions
Eventual consistency within replicaset partition infowhen using asyncronous replication
Immediate Consistency within single instance
Sequential consistency including linearizable read within replicaset partition infowhen using Raft
Foreign keys infoReferential integritynoyes infovia ReferenceProperties or Ancestor pathsnonoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsnonoACID, with serializable isolation and linearizable read (within partition); Configurable MVCC (within partition); No cross-shard distributed transactions
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes, cooperative multitasking
Durability infoSupport for making data persistentyesyesyesyesyes, write ahead logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.nononoyes, full featured in-memory storage engine with persistence
User concepts infoAccess controlAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory AuthenticationUsers with fine-grained authorization conceptAccess Control Lists
Mutual TLS authentication for Tarantol Enterprise
Password based authentication
Role-based access control (RBAC) and LDAP for Tarantol Enterprise
Users and Roles

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More resources
AxibaseGoogle Cloud DatastoreMicrosoft Azure Data ExplorerSiteWhereTarantool
DB-Engines blog posts

Data processing speed and reliability: in-memory synchronous replication
9 November 2021,  Vladimir Perepelytsya, Tarantool (sponsor) 

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