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DBMS > Apache IoTDB vs. FatDB vs. Google Cloud Datastore vs. Spark SQL

System Properties Comparison Apache IoTDB vs. FatDB vs. Google Cloud Datastore vs. Spark SQL

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFatDB  Xexclude from comparisonGoogle Cloud Datastore  Xexclude from comparisonSpark SQL  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 PlatformSpark SQL is a component on top of 'Spark Core' for structured data processing
Primary database modelTime Series DBMSDocument store
Key-value store
Document storeRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score4.36
Rank#72  Overall
#12  Document stores
Score18.04
Rank#33  Overall
#20  Relational DBMS
Websiteiotdb.apache.orgcloud.google.com/­datastorespark.apache.org/­sql
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmlcloud.google.com/­datastore/­docsspark.apache.org/­docs/­latest/­sql-programming-guide.html
DeveloperApache Software FoundationFatCloudGoogleApache Software Foundation
Initial release2018201220082014
Current release1.1.0, April 20233.5.0 ( 2.13), September 2023
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercialcommercialOpen Source infoApache 2.0
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 languageJavaC#Scala
Server operating systemsAll OS with a Java VM (>= 1.8)WindowshostedLinux
OS X
Windows
Data schemeyesschema-freeschema-freeyes
Typing infopredefined data types such as float or dateyesyesyes, details hereyes
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 indexesyesyesyesno
SQL infoSupport of SQLSQL-like query languageno infoVia inetgration in SQL ServerSQL-like query language (GQL)SQL-like DML and DDL statements
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
JDBC
ODBC
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C#.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Java
Python
R
Scala
Server-side scripts infoStored proceduresyesyes infovia applicationsusing Google App Engineno
Triggersyesyes infovia applicationsCallbacks using the Google Apps Engineno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)ShardingShardingyes, utilizing Spark Core
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 Paxosnone
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesyes infousing Google Cloud Dataflow
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.
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 Transactionsno
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.yesnono
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)no

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More resources
Apache IoTDBFatDBGoogle Cloud DatastoreSpark SQL
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