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DBMS > Apache IoTDB vs. FatDB vs. Google Cloud Datastore vs. RDF4J vs. SwayDB

System Properties Comparison Apache IoTDB vs. FatDB vs. Google Cloud Datastore vs. RDF4J vs. SwayDB

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonRDF4J infoformerly known as Sesame  Xexclude from comparisonSwayDB  Xexclude from comparison
FatDB/FatCloud has ceased operations as a company with February 2014. FatDB is discontinued and excluded from the 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 FlinkA .NET NoSQL DBMS that can integrate with and extend SQL Server.Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud PlatformRDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage
Primary database modelTime Series DBMSDocument store
Key-value store
Document storeRDF storeKey-value store
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#159  Overall
#14  Time Series DBMS
Score4.13
Rank#71  Overall
#12  Document stores
Score0.72
Rank#222  Overall
#9  RDF stores
Score0.00
Rank#384  Overall
#60  Key-value stores
Websiteiotdb.apache.orgcloud.google.com/­datastorerdf4j.orgswaydb.simer.au
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­datastore/­docsrdf4j.org/­documentation
DeveloperApache Software FoundationFatCloudGoogleSince 2016 officially forked into an Eclipse project, former developer was Aduna Software.Simer Plaha
Initial release20182012200820042018
Current release1.1.0, April 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoEclipse Distribution License (EDL), v1.0.Open Source infoGNU Affero GPL V3.0
Cloud-based only infoOnly available as a cloud servicenonoyesnono
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC#JavaScala
Server operating systemsAll OS with a Java VM (>= 1.8)WindowshostedLinux
OS X
Unix
Windows
Data schemeyesschema-freeschema-freeyes infoRDF Schemasschema-free
Typing infopredefined data types such as float or dateyesyesyes, details hereyesno
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 indexesyesyesyesyesno
SQL infoSupport of SQLSQL-like query languageno infoVia inetgration in SQL ServerSQL-like query language (GQL)nono
APIs and other access methodsJDBC
Native API
.NET Client API
LINQ
RESTful HTTP API
RPC
Windows WCF Bindings
gRPC (using protocol buffers) API
RESTful HTTP/JSON API
Java API
RIO infoRDF Input/Output
Sail API
SeRQL infoSesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
PHP
Python
Java
Kotlin
Scala
Server-side scripts infoStored proceduresyesyes infovia applicationsusing Google App Engineyesno
Triggersyesyes infovia applicationsCallbacks using the Google Apps Engineyesno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingnonenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasselectable replication factorMulti-source replication using Paxosnonenone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyes infousing Google Cloud Dataflownono
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual Consistency
Immediate Consistency
Immediate Consistency or Eventual Consistency depending on type of query and configuration infoStrong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.Immediate Consistency
Foreign keys infoReferential integritynonoyes infovia ReferenceProperties or Ancestor pathsno
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanonoACID infoSerializable Isolation within Transactions, Read Committed outside of TransactionsACID infoIsolation support depends on the API usedAtomic execution of operations
Concurrency infoSupport for concurrent manipulation of datayesyesyesyesyes
Durability infoSupport for making data persistentyesyesyesyes infoin-memory storage is supported as wellyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnoyes
User concepts infoAccess controlyesno infoCan implement custom security layer via applicationsAccess rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)nono

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More resources
Apache IoTDBFatDBGoogle Cloud DatastoreRDF4J infoformerly known as SesameSwayDB
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