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DBMS > Apache IoTDB vs. Microsoft Azure Table Storage vs. Stardog vs. Warp 10

System Properties Comparison Apache IoTDB vs. Microsoft Azure Table Storage vs. Stardog vs. Warp 10

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonStardog  Xexclude from comparisonWarp 10  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 FlinkA Wide Column Store for rapid development using massive semi-structured datasetsEnterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualizationTimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase
Primary database modelTime Series DBMSWide column storeGraph DBMS
RDF store
Time Series DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Score2.02
Rank#123  Overall
#11  Graph DBMS
#6  RDF stores
Score0.07
Rank#349  Overall
#32  Time Series DBMS
Websiteiotdb.apache.orgazure.microsoft.com/­en-us/­services/­storage/­tableswww.stardog.comwww.warp10.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.stardog.comwww.warp10.io/­content/­02_Getting_started
DeveloperApache Software FoundationMicrosoftStardog-UnionSenX
Initial release2018201220102015
Current release1.1.0, April 20237.3.0, May 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercial info60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/studentsOpen Source infoApache License 2.0
Cloud-based only infoOnly available as a cloud servicenoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaJavaJava
Server operating systemsAll OS with a Java VM (>= 1.8)hostedLinux
macOS
Windows
Linux
OS X
Windows
Data schemeyesschema-freeschema-free and OWL/RDFS-schema supportschema-free
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.nonono infoImport/export of XML data possibleno
Secondary indexesyesnoyes infosupports real-time indexing in full-text and geospatialno
SQL infoSupport of SQLSQL-like query languagenoYes, compatible with all major SQL variants through dedicated BI/SQL Serverno
APIs and other access methodsJDBC
Native API
RESTful HTTP APIGraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3
HTTP API
Jupyter
WebSocket
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby
Server-side scripts infoStored proceduresyesnouser defined functions and aggregates, HTTP Server extensions in Javayes infoWarpScript
Triggersyesnoyes infovia event handlersno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoImplicit feature of the cloud servicenoneSharding infobased on HBase
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasyes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.Multi-source replication in HA-Clusterselectable replication factor infobased on HBase
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 in HA-ClusterImmediate Consistency infobased on HBase
Foreign keys infoReferential integritynonoyes inforelationships in graphsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanooptimistic lockingACIDno
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyesyes
User concepts infoAccess controlyesAccess rights based on private key authentication or shared access signaturesAccess rights for users and rolesMandatory use of cryptographic tokens, containing fine-grained authorizations

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More resources
Apache IoTDBMicrosoft Azure Table StorageStardogWarp 10
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