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DBMS > Adabas vs. Axibase vs. CouchDB vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer

System Properties Comparison Adabas vs. Axibase vs. CouchDB vs. Google Cloud Datastore vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines
NameAdabas infodenotes "adaptable data base"  Xexclude from comparisonAxibase  Xexclude from comparisonCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparison
DescriptionOLTP - DBMS for mainframes and Linux/Unix/Windows environments infoused typically together with the Natural programming platformScalable TimeSeries DBMS based on HBase with integrated rule engine and visualizationA native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformFully managed big data interactive analytics platform
Primary database modelMultivalue DBMSTime Series DBMSDocument storeDocument storeRelational DBMS infocolumn oriented
Secondary database modelsSpatial DBMS infousing the Geocouch extensionDocument 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
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score3.17
Rank#94  Overall
#1  Multivalue DBMS
Score0.29
Rank#292  Overall
#25  Time Series DBMS
Score9.30
Rank#45  Overall
#7  Document stores
Score4.47
Rank#76  Overall
#12  Document stores
Score4.38
Rank#77  Overall
#41  Relational DBMS
Websitewww.softwareag.com/­en_corporate/­platform/­adabas-natural.htmlaxibase.com/­docs/­atsd/­financecouchdb.apache.orgcloud.google.com/­datastoreazure.microsoft.com/­services/­data-explorer
Technical documentationdocs.couchdb.org/­en/­stablecloud.google.com/­datastore/­docsdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperSoftware AGAxibase CorporationApache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerGoogleMicrosoft
Initial release19712013200520082019
Current release155853.3.3, December 2023cloud service with continuous releases
License infoCommercial or Open Sourcecommercialcommercial infoCommunity Edition (single node) is free, Enterprise Edition (distributed) is paidOpen Source infoApache version 2commercialcommercial
Cloud-based only infoOnly available as a cloud servicenononoyesyes
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaErlang
Server operating systemsBS2000
Linux
Unix
Windows
z/OS
z/VSE
LinuxAndroid
BSD
Linux
OS X
Solaris
Windows
hostedhosted
Data schemeyesyesschema-freeschema-freeFixed schema with schema-less datatypes (dynamic)
Typing infopredefined data types such as float or dateyesyes infoshort, integer, long, float, double, decimal, stringnoyes, details hereyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types
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.nonononoyes
Secondary indexesyesnoyes infovia viewsyesall fields are automatically indexed
SQL infoSupport of SQLyes infowith add-on product Adabas SQL GatewaySQL-like query languagenoSQL-like query language (GQL)Kusto Query Language (KQL), SQL subset
APIs and other access methodsHTTP API infowith add-on software Adabas SOA Gateway
SOAP-based API infowith add-on software Adabas SOA Gateway
JDBC
Proprietary protocol (Network API)
RESTful HTTP API
RESTful HTTP/JSON APIgRPC (using protocol buffers) API
RESTful HTTP/JSON API
Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Supported programming languagesNaturalGo
Java
PHP
Python
R
Ruby
C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Server-side scripts infoStored proceduresin NaturalyesView functions in JavaScriptusing Google App EngineYes, possible languages: KQL, Python, R
TriggersnoyesyesCallbacks using the Google Apps Engineyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicy
Partitioning methods infoMethods for storing different data on different nodesyes, with additonal products like Adabas Cluster Services, Adabas Parallel Services, Adabas VistaShardingSharding infoimproved architecture with release 2.0ShardingSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesyes, with add-on product Event ReplicatorSource-replica replicationMulti-source replication
Source-replica replication
Multi-source replication using Paxosyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
MapReduce infoOffers an API for user-defined Map/Reduce methodsnoyesyesyes infousing Google Cloud DataflowSpark connector (open source): github.com/­Azure/­azure-kusto-spark
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyEventual 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.Eventual Consistency
Immediate Consistency
Foreign keys infoReferential integritynononoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of dataACIDnono infoatomic operations within a single document possibleACID infoSerializable Isolation within Transactions, Read Committed outside of Transactionsno
Concurrency infoSupport for concurrent manipulation of datayesyesyes infostrategy: optimistic lockingyesyes
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.nononono
User concepts infoAccess controlonly with OS-specific tools (e.g. IBM RACF, CA Top Secret)Access rights for users can be defined per databaseAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)Azure Active Directory Authentication

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More resources
Adabas infodenotes "adaptable data base"AxibaseCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"Google Cloud DatastoreMicrosoft Azure Data Explorer
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