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DBMS > Apache IoTDB vs. GridGain vs. Microsoft Azure Table Storage vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. GridGain vs. Microsoft Azure Table Storage vs. Teradata Aster

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonGridGain  Xexclude from comparisonMicrosoft Azure Table Storage  Xexclude from comparisonTeradata Aster  Xexclude from comparison
Teradata Aster has been integrated into other Teradata systems and therefore will be 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 FlinkGridGain is an in-memory computing platform, built on Apache IgniteA Wide Column Store for rapid development using massive semi-structured datasetsPlatform for big data analytics on multistructured data sources and types
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Wide column storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.18
Rank#173  Overall
#15  Time Series DBMS
Score1.47
Rank#154  Overall
#26  Key-value stores
#72  Relational DBMS
Score4.48
Rank#75  Overall
#6  Wide column stores
Websiteiotdb.apache.orgwww.gridgain.comazure.microsoft.com/­en-us/­services/­storage/­tables
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlwww.gridgain.com/­docs/­index.html
DeveloperApache Software FoundationGridGain Systems, Inc.MicrosoftTeradata
Initial release2018200720122005
Current release1.1.0, April 2023GridGain 8.5.1
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaJava, C++, .Net
Server operating systemsAll OS with a Java VM (>= 1.8)Linux
OS X
Solaris
Windows
hostedLinux
Data schemeyesyesschema-freeFlexible Schema (defined schema, partial schema, schema free) infodefined schema within the relational store; partial schema or schema free in the Aster File Store
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.noyesnoyes infoin Aster File Store
Secondary indexesyesyesnoyes
SQL infoSupport of SQLSQL-like query languageANSI-99 for query and DML statements, subset of DDLnoyes
APIs and other access methodsJDBC
Native API
HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data
RESTful HTTP APIADO.NET
JDBC
ODBC
OLE DB
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C#
C++
Java
PHP
Python
Ruby
Scala
.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby
C
C#
C++
Java
Python
R
Server-side scripts infoStored proceduresyesyes (compute grid and cache interceptors can be used instead)noR packages
Triggersyesyes (cache interceptors and events)nono
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingSharding infoImplicit feature of the cloud serviceSharding
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 (replicated cache)yes infoimplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.yes infoDimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyes (compute grid and hadoop accelerator)noyes infoSQL Map-Reduce Framework
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate ConsistencyImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynononono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDoptimistic lockingACID
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.yesyesnono
User concepts infoAccess controlyesSecurity Hooks for custom implementationsAccess rights based on private key authentication or shared access signaturesfine grained access rights according to SQL-standard

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More resources
Apache IoTDBGridGainMicrosoft Azure Table StorageTeradata Aster
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