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

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

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonTimescaleDB  Xexclude from comparisonYaacomo  Xexclude from comparison
Yaacomo seems to be discontinued and is removed 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 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 time series DBMS optimized for fast ingest and complex queries, based on PostgreSQLOpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing
Primary database modelTime Series DBMSRelational DBMSTime Series DBMSRelational 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
Score4.46
Rank#71  Overall
#5  Time Series DBMS
Websiteiotdb.apache.orgwww.microsoft.com/­en-us/­microsoft-365/­accesswww.timescale.comyaacomo.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldeveloper.microsoft.com/­en-us/­accessdocs.timescale.com
DeveloperApache Software FoundationMicrosoftTimescaleQ2WEB GmbH
Initial release2018199220172009
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 Source infoApache 2.0commercial
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 DLLsLinux
OS X
Windows
Android
Linux
Windows
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesnumerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data typesyes
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.noyesno
Secondary indexesyesyesyesyes
SQL infoSupport of SQLSQL-like query languageyes infobut not compliant to any SQL standardyes infofull PostgreSQL SQL syntaxyes
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
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.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-engineuser 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-engineyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneyes, across time and space (hash partitioning) attributeshorizontal 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 with hot standby and reads on replicas infoSource-replica replication
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 integritynoyesyesyes
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 datayesyesyesyes
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.yesnoyes
User concepts infoAccess controlyesno infoa simple user-level security was built in till version Access 2003fine grained access rights according to SQL-standardfine grained access rights according to SQL-standard

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More resources
Apache IoTDBMicrosoft AccessTimescaleDBYaacomo
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