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DBMS > Apache IoTDB vs. CouchDB vs. Databricks vs. Faircom EDGE

System Properties Comparison Apache IoTDB vs. CouchDB vs. Databricks vs. Faircom EDGE

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"  Xexclude from comparisonDatabricks  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  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 native JSON - document store inspired by Lotus Notes, scalable from globally distributed server-clusters down to mobile phones.The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments
Primary database modelTime Series DBMSDocument storeDocument store
Relational DBMS
Key-value store
Relational DBMS
Secondary database modelsSpatial DBMS infousing the Geocouch extension
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score8.30
Rank#47  Overall
#7  Document stores
Score81.08
Rank#15  Overall
#2  Document stores
#10  Relational DBMS
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Websiteiotdb.apache.orgcouchdb.apache.orgwww.databricks.comwww.faircom.com/­products/­faircom-edge
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.couchdb.org/­en/­stabledocs.databricks.comdocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html
DeveloperApache Software FoundationApache Software Foundation infoApache top-level project, originally developed by Damien Katz, a former Lotus Notes developerDatabricksFairCom Corporation
Initial release2018200520131979
Current release1.1.0, April 20233.3.3, December 2023V3, October 2020
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoApache version 2commercialcommercial infoRestricted, free version available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaErlangANSI C, C++
Server operating systemsAll OS with a Java VM (>= 1.8)Android
BSD
Linux
OS X
Solaris
Windows
hostedAndroid
Linux infoARM, x86
Raspbian
Windows
Data schemeyesschema-freeFlexible Schema (defined schema, partial schema, schema free)Flexible Schema (defined schema, partial schema, schema free)
Typing infopredefined data types such as float or dateyesnoyes, ANSI Standard SQL Types
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.nonoyesyes
Secondary indexesyesyes infovia viewsyesyes
SQL infoSupport of SQLSQL-like query languagenowith Databricks SQLyes infoANSI SQL queries
APIs and other access methodsJDBC
Native API
RESTful HTTP/JSON APIJDBC
ODBC
RESTful HTTP API
ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
ColdFusion
Erlang
Haskell
Java
JavaScript
Lisp
Lua
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
Ruby
Smalltalk
Python
R
Scala
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
Server-side scripts infoStored proceduresyesView functions in JavaScriptuser defined functions and aggregatesyes info.Net, JavaScript, C/C++
Triggersyesyesyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)Sharding infoimproved architecture with release 2.0File partitioning infoCustomizable business rules for partitioning
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasMulti-source replication
Source-replica replication
yesyes infoSynchronous and asynchronous realtime replication based on transaction logs
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and Sparkyesno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Eventual ConsistencyImmediate ConsistencyImmediate Consistency
Tunable Consistency
Foreign keys infoReferential integritynonoyes infowhen using SQL
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanono infoatomic operations within a single document possibleACIDACID
Concurrency infoSupport for concurrent manipulation of datayesyes infostrategy: optimistic lockingyesyes infoacross SQL and NoSQL
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.yesnonoyes
User concepts infoAccess controlyesAccess rights for users can be defined per databaseFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.
More information provided by the system vendor
Apache IoTDBCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"DatabricksFaircom EDGE infoformerly c-treeEDGE
Specific characteristicsSupported database models : In addition to the Document store and Relational DBMS...
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Apache IoTDBCouchDB infostands for "Cluster Of Unreliable Commodity Hardware"DatabricksFaircom EDGE infoformerly c-treeEDGE
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