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DBMS > Apache IoTDB vs. Fauna vs. Microsoft Access vs. Microsoft Azure Table Storage

System Properties Comparison Apache IoTDB vs. Fauna vs. Microsoft Access vs. Microsoft Azure Table Storage

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFauna infopreviously named FaunaDB  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonMicrosoft Azure Table Storage  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 FlinkFauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.Microsoft 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 Wide Column Store for rapid development using massive semi-structured datasets
Primary database modelTime Series DBMSDocument store
Graph DBMS
Relational DBMS
Time Series DBMS
Relational DBMSWide column store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score1.55
Rank#151  Overall
#26  Document stores
#14  Graph DBMS
#71  Relational DBMS
#13  Time Series DBMS
Score101.16
Rank#11  Overall
#8  Relational DBMS
Score4.04
Rank#77  Overall
#6  Wide column stores
Websiteiotdb.apache.orgfauna.comwww.microsoft.com/­en-us/­microsoft-365/­accessazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.fauna.comdeveloper.microsoft.com/­en-us/­access
DeveloperApache Software FoundationFauna, Inc.MicrosoftMicrosoft
Initial release2018201419922012
Current release1.1.0, April 20231902 (16.0.11328.20222), March 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infoBundled with Microsoft Officecommercial
Cloud-based only infoOnly available as a cloud servicenoyesnoyes
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaScalaC++
Server operating systemsAll OS with a Java VM (>= 1.8)hostedWindows infoNot a real database server, but making use of DLLshosted
Data schemeyesschema-freeyesschema-free
Typing infopredefined data types such as float or dateyesnoyesyes
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.nonono
Secondary indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languagenoyes infobut not compliant to any SQL standardno
APIs and other access methodsJDBC
Native API
RESTful HTTP APIADO.NET
DAO
ODBC
OLE DB
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
Server-side scripts infoStored proceduresyesuser defined functionsyes infosince Access 2010 using the ACE-engineno
Triggersyesnoyes infosince Access 2010 using the ACE-engineno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)horizontal partitioning infoconsistent hashingnoneSharding infoImplicit feature of the cloud service
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replicationnoneyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.
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 integritynoyesyesno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDACID infobut no files for transaction loggingoptimistic locking
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyes infobut no files for transaction loggingyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnono
User concepts infoAccess controlyesIdentity management, authentication, and access controlno infoa simple user-level security was built in till version Access 2003Access rights based on private key authentication or shared access signatures

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More resources
Apache IoTDBFauna infopreviously named FaunaDBMicrosoft AccessMicrosoft Azure Table Storage
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