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DBMS > Apache IoTDB vs. Firebolt vs. Microsoft Access vs. Riak TS

System Properties Comparison Apache IoTDB vs. Firebolt vs. Microsoft Access vs. Riak TS

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFirebolt  Xexclude from comparisonMicrosoft Access  Xexclude from comparisonRiak TS  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 FlinkHighly scalable cloud data warehouse and analytics product infoForked from ClickhouseMicrosoft 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.)Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV
Primary database modelTime Series DBMSRelational DBMSRelational DBMSTime Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score1.73
Rank#140  Overall
#63  Relational DBMS
Score101.16
Rank#11  Overall
#8  Relational DBMS
Score0.28
Rank#307  Overall
#27  Time Series DBMS
Websiteiotdb.apache.orgwww.firebolt.iowww.microsoft.com/­en-us/­microsoft-365/­access
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.firebolt.iodeveloper.microsoft.com/­en-us/­accesswww.tiot.jp/­riak-docs/­riak/­ts/­latest
DeveloperApache Software FoundationFirebolt Analytics Inc.MicrosoftOpen Source, formerly Basho Technologies
Initial release2018202019922015
Current release1.1.0, April 20231902 (16.0.11328.20222), March 20193.0.0, September 2022
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial infoBundled with Microsoft OfficeOpen Source
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++Erlang
Server operating systemsAll OS with a Java VM (>= 1.8)hostedWindows infoNot a real database server, but making use of DLLsLinux
OS X
Data schemeyesyesyesschema-free
Typing infopredefined data types such as float or dateyesyesyesno
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 indexesyesyesyesrestricted
SQL infoSupport of SQLSQL-like query languageyesyes infobut not compliant to any SQL standardyes, limited
APIs and other access methodsJDBC
Native API
.Net
ODBC
RESTful HTTP API
ADO.NET
DAO
ODBC
OLE DB
HTTP API
Native Erlang Interface
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
Go
JavaScript (Node.js)
Python
C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET
C infounofficial client library
C#
C++ infounofficial client library
Clojure infounofficial client library
Dart infounofficial client library
Erlang
Go infounofficial client library
Groovy infounofficial client library
Haskell infounofficial client library
Java
JavaScript infounofficial client library
Lisp infounofficial client library
Perl infounofficial client library
PHP
Python
Ruby
Scala infounofficial client library
Smalltalk infounofficial client library
Server-side scripts infoStored proceduresyesnoyes infosince Access 2010 using the ACE-engineErlang
Triggersyesnoyes infosince Access 2010 using the ACE-engineyes infopre-commit hooks and post-commit hooks
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasdepending on storage layernoneselectable replication factor
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparknoyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency
Foreign keys infoReferential integritynoyesno infolinks between datasets can be stored
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACID infobut no files for transaction loggingno
Concurrency infoSupport for concurrent manipulation of datayesyesyes
Durability infoSupport for making data persistentyesyes 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.yes
User concepts infoAccess controlyesno infoa simple user-level security was built in till version Access 2003no

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More resources
Apache IoTDBFireboltMicrosoft AccessRiak TS
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