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DBMS > Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. Percona Server for MySQL vs. RethinkDB vs. STSdb

System Properties Comparison Google Cloud Bigtable vs. Microsoft Azure Data Explorer vs. Percona Server for MySQL vs. RethinkDB vs. STSdb

Editorial information provided by DB-Engines
NameGoogle Cloud Bigtable  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonPercona Server for MySQL  Xexclude from comparisonRethinkDB  Xexclude from comparisonSTSdb  Xexclude from comparison
DescriptionGoogle's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.Fully managed big data interactive analytics platformEnhanced drop-in replacement for MySQL based on XtraDB or TokuDB storage engines with improved performance and additional diagnostic and management features.DBMS for the Web with a mechanism to push updated query results to applications in realtime.Key-Value Store with special method for indexing infooptimized for high performance using a special indexing method
Primary database modelKey-value store
Wide column store
Relational DBMS infocolumn orientedRelational DBMSDocument storeKey-value store
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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.26
Rank#92  Overall
#13  Key-value stores
#8  Wide column stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Score2.03
Rank#122  Overall
#57  Relational DBMS
Score2.74
Rank#105  Overall
#19  Document stores
Score0.04
Rank#360  Overall
#52  Key-value stores
Websitecloud.google.com/­bigtableazure.microsoft.com/­services/­data-explorerwww.percona.com/­software/­mysql-database/­percona-serverrethinkdb.comgithub.com/­STSSoft/­STSdb4
Technical documentationcloud.google.com/­bigtable/­docsdocs.microsoft.com/­en-us/­azure/­data-explorerwww.percona.com/­downloads/­Percona-Server-LATESTrethinkdb.com/­docs
DeveloperGoogleMicrosoftPerconaThe Linux Foundation infosince July 2017STS Soft SC
Initial release20152019200820092011
Current releasecloud service with continuous releases8.0.36-28, 20242.4.1, August 20204.0.8, September 2015
License infoCommercial or Open SourcecommercialcommercialOpen Source infoGPL version 2Open Source infoApache Version 2Open Source infoGPLv2, commercial license available
Cloud-based only infoOnly available as a cloud serviceyesyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC and C++C++C#
Server operating systemshostedhostedLinuxLinux
OS X
Windows
Windows
Data schemeschema-freeFixed schema with schema-less datatypes (dynamic)yesschema-freeyes
Typing infopredefined data types such as float or datenoyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyesyes infostring, binary, float, bool, date, geometryyes infoprimitive types and user defined types (classes)
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.noyesyesno
Secondary indexesnoall fields are automatically indexedyesyesno
SQL infoSupport of SQLnoKusto Query Language (KQL), SQL subsetyesnono
APIs and other access methodsgRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
ADO.NET
JDBC
ODBC
.NET Client API
Supported programming languagesC#
C++
Go
Java
JavaScript (Node.js)
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Ada
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl
C infocommunity-supported driver
C# infocommunity-supported driver
C++ infocommunity-supported driver
Clojure infocommunity-supported driver
Dart infocommunity-supported driver
Erlang infocommunity-supported driver
Go infocommunity-supported driver
Haskell infocommunity-supported driver
Java infoofficial driver
JavaScript (Node.js) infoofficial driver
Lisp infocommunity-supported driver
Lua infocommunity-supported driver
Objective-C infocommunity-supported driver
Perl infocommunity-supported driver
PHP infocommunity-supported driver
Python infoofficial driver
Ruby infoofficial driver
Scala infocommunity-supported driver
C#
Java
Server-side scripts infoStored proceduresnoYes, possible languages: KQL, Python, Ryesno
Triggersnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyesClient-side triggers through changefeedsno
Partitioning methods infoMethods for storing different data on different nodesShardingSharding infoImplicit feature of the cloud serviceSharding inforange basednone
Replication methods infoMethods for redundantly storing data on multiple nodesInternal replication in Colossus, and regional replication between two clusters in different zonesyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication
Source-replica replication
XtraDB Cluster
Source-replica replicationnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsyesSpark connector (open source): github.com/­Azure/­azure-kusto-sparknoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)Eventual Consistency
Immediate Consistency
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataAtomic single-row operationsnoACIDAtomic single-document operationsno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes infoMVCC basedyes
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.nonoyesno
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 concept infono user groups or rolesyes infousers and table-level permissionsno

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More resources
Google Cloud BigtableMicrosoft Azure Data ExplorerPercona Server for MySQLRethinkDBSTSdb
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