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DBMS > Apache IoTDB vs. GeoSpock vs. Microsoft Access vs. Netezza vs. Postgres-XL

System Properties Comparison Apache IoTDB vs. GeoSpock vs. Microsoft Access vs. Netezza vs. Postgres-XL

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGeoSpock  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonNetezza infoAlso called PureData System for Analytics by IBM  Xexclude from comparisonPostgres-XL  Xexclude from comparison
GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.
DescriptionAn IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and FlinkSpatial and temporal data processing engine for extreme data scaleMicrosoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries. infoThe Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)Data warehouse and analytics appliance part of IBM PureSystemsBased on PostgreSQL enhanced with MPP and write-scale-out cluster features
Primary database modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsTime Series DBMSDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score104.92
Rank#11  Overall
#8  Relational DBMS
Score9.06
Rank#46  Overall
#29  Relational DBMS
Score0.49
Rank#256  Overall
#117  Relational DBMS
Websiteiotdb.apache.orggeospock.comwww.microsoft.com/­en-us/­microsoft-365/­accesswww.ibm.com/­products/­netezzawww.postgres-xl.org
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldeveloper.microsoft.com/­en-us/­accesswww.postgres-xl.org/­documentation
DeveloperApache Software FoundationGeoSpockMicrosoftIBM
Initial release2018199220002014 infosince 2012, originally named StormDB
Current release1.1.0, April 20232.0, September 20191902 (16.0.11328.20222), March 201910 R1, October 2018
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infoBundled with Microsoft OfficecommercialOpen Source infoMozilla public license
Cloud-based only infoOnly available as a cloud servicenoyesnonono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJava, JavascriptC++C
Server operating systemsAll OS with a Java VM (>= 1.8)hostedWindows infoNot a real database server, but making use of DLLsLinux infoincluded in applianceLinux
macOS
Data schemeyesyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyesyes
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.nonoyes infoXML type, but no XML query functionality
Secondary indexesyestemporal, categoricalyesyesyes
SQL infoSupport of SQLSQL-like query languageANSI SQL for query only (using Presto)yes infobut not compliant to any SQL standardyesyes infodistributed, parallel query execution
APIs and other access methodsJDBC
Native API
JDBCADO.NET
DAO
ODBC
OLE DB
JDBC
ODBC
OLE DB
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
C
C++
Fortran
Java
Lua
Perl
Python
R
.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
Server-side scripts infoStored proceduresyesnoyes infosince Access 2010 using the ACE-engineyesuser defined functions
Triggersyesnoyes infosince Access 2010 using the ACE-enginenoyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Automatic shardingnoneShardinghorizontal partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneSource-replica replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonoyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynonoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infobut no files for transaction loggingACIDACID infoMVCC
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyes infobut no files for transaction loggingyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlyesAccess rights for users can be defined per tableno infoa simple user-level security was built in till version Access 2003Users with fine-grained authorization conceptfine grained access rights according to SQL-standard

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More resources
Apache IoTDBGeoSpockMicrosoft AccessNetezza infoAlso called PureData System for Analytics by IBMPostgres-XL
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