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DBMS > Apache IoTDB vs. Microsoft Access vs. Realm vs. TimescaleDB

System Properties Comparison Apache IoTDB vs. Microsoft Access vs. Realm vs. TimescaleDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonRealm  Xexclude from comparisonTimescaleDB  Xexclude from comparison
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 FlinkMicrosoft 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.)A DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core DataA time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL
Primary database modelTime Series DBMSRelational DBMSDocument storeTime Series DBMS
Secondary database modelsRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score101.16
Rank#11  Overall
#8  Relational DBMS
Score7.41
Rank#52  Overall
#8  Document stores
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websiteiotdb.apache.orgwww.microsoft.com/­en-us/­microsoft-365/­accessrealm.iowww.timescale.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldeveloper.microsoft.com/­en-us/­accessrealm.io/­docsdocs.timescale.com
DeveloperApache Software FoundationMicrosoftRealm, acquired by MongoDB in May 2019Timescale
Initial release2018199220142017
Current release1.1.0, April 20231902 (16.0.11328.20222), March 20192.15.0, May 2024
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoBundled with Microsoft OfficeOpen SourceOpen Source infoApache 2.0
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C
Server operating systemsAll OS with a Java VM (>= 1.8)Windows infoNot a real database server, but making use of DLLsAndroid
Backend: server-less
iOS
Windows
Linux
OS X
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex 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.nonoyes
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyes infobut not compliant to any SQL standardnoyes infofull PostgreSQL SQL syntax
APIs and other access methodsJDBC
Native API
ADO.NET
DAO
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
.Net
Java infowith Android only
Objective-C
React Native
Swift
.Net
C
C++
Delphi
Java infoJDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl
Server-side scripts infoStored proceduresyesyes infosince Access 2010 using the ACE-engineno inforuns within the applications so server-side scripts are unnecessaryuser defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell
Triggersyesyes infosince Access 2010 using the ACE-engineyes infoChange Listenersyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)nonenoneyes, across time and space (hash partitioning) attributes
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnonenoneSource-replica replication with hot standby and reads on replicas info
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknonono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate Consistency
Foreign keys infoReferential integritynoyesnoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infobut no files for transaction loggingACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes 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.yesyes infoIn-Memory realmno
User concepts infoAccess controlyesno infoa simple user-level security was built in till version Access 2003yesfine grained access rights according to SQL-standard

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More resources
Apache IoTDBMicrosoft AccessRealmTimescaleDB
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