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DBMS > Apache IoTDB vs. Databend vs. Fujitsu Enterprise Postgres vs. Microsoft Access

System Properties Comparison Apache IoTDB vs. Databend vs. Fujitsu Enterprise Postgres vs. Microsoft Access

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonDatabend  Xexclude from comparisonFujitsu Enterprise Postgres  Xexclude from comparisonMicrosoft Access  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 FlinkAn open-source, elastic, and workload-aware cloud data warehouse designed to meet businesses' massive-scale analytics needs at low cost and with low complexityEnterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.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.)
Primary database modelTime Series DBMSRelational DBMSRelational DBMSRelational DBMS
Secondary database modelsDocument store
Spatial DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score0.28
Rank#287  Overall
#131  Relational DBMS
Score0.22
Rank#304  Overall
#137  Relational DBMS
Score93.76
Rank#12  Overall
#8  Relational DBMS
Websiteiotdb.apache.orggithub.com/­datafuselabs/­databend
www.databend.com
www.postgresql.fastware.comwww.microsoft.com/­en-us/­microsoft-365/­access
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.databend.comwww.postgresql.fastware.com/­product-manualsdeveloper.microsoft.com/­en-us/­access
DeveloperApache Software FoundationDatabend LabsPostgreSQL Global Development Group, Fujitsu Australia Software TechnologyMicrosoft
Initial release201820211992
Current release1.1.0, April 20231.0.59, April 2023Fujitsu Enterprise Postgres 14, January 20221902 (16.0.11328.20222), March 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache Version 2.0commercialcommercial infoBundled with Microsoft Office
Cloud-based only infoOnly available as a cloud servicenononono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaRustCC++
Server operating systemsAll OS with a Java VM (>= 1.8)hosted
Linux
macOS
Linux
Windows
Windows infoNot a real database server, but making use of DLLs
Data schemeyesyesyesyes
Typing infopredefined data types such as float or dateyesyesyesyes
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.nono
Secondary indexesyesnoyesyes
SQL infoSupport of SQLSQL-like query languageyesyesyes infobut not compliant to any SQL standard
APIs and other access methodsJDBC
Native API
CLI Client
JDBC
RESTful HTTP API
ADO.NET
JDBC
native C library
ODBC
streaming API for large objects
ADO.NET
DAO
ODBC
OLE DB
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Go
Java
JavaScript (Node.js)
Python
Rust
.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
Server-side scripts infoStored proceduresyesnouser defined functionsyes infosince Access 2010 using the ACE-engine
Triggersyesnoyesyes infosince Access 2010 using the ACE-engine
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)nonepartitioning by range, list and by hashnone
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 replicationnone
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 integritynonoyesyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoyesACIDACID infobut no files for transaction logging
Concurrency infoSupport for concurrent manipulation of datayesyesyes, multi-version concurrency control (MVCC)yes
Durability infoSupport for making data persistentyesyesyesyes infobut no files for transaction logging
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yes
User concepts infoAccess controlyesUsers with fine-grained authorization concept, user rolesfine grained access rights according to SQL-standardno infoa simple user-level security was built in till version Access 2003

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More resources
Apache IoTDBDatabendFujitsu Enterprise PostgresMicrosoft Access
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