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DBMS > Apache IoTDB vs. Faircom EDGE vs. Microsoft Azure AI Search vs. Microsoft Azure Data Explorer vs. Sqrrl

System Properties Comparison Apache IoTDB vs. Faircom EDGE vs. Microsoft Azure AI Search vs. Microsoft Azure Data Explorer vs. Sqrrl

Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonFaircom EDGE infoformerly c-treeEDGE  Xexclude from comparisonMicrosoft Azure AI Search  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonSqrrl  Xexclude from comparison
Sqrrl has been acquired by Amazon and became a part of Amazon Web Services. It has been 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 FlinkFairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environmentsSearch-as-a-service for web and mobile app developmentFully managed big data interactive analytics platformAdaptable, secure NoSQL built on Apache Accumulo
Primary database modelTime Series DBMSKey-value store
Relational DBMS
Search engineRelational DBMS infocolumn orientedDocument store
Graph DBMS
Key-value store
Wide column store
Secondary database modelsVector DBMSDocument store infoIf a column is of type dynamic docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-types/­dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store infothis is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine infosupport for complex search expressions docs.microsoft.com/­en-us/­azure/­kusto/­query/­parseoperator FTS, Geospatial docs.microsoft.com/­en-us/­azure/­kusto/­query/­geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS infosee docs.microsoft.com/­en-us/­azure/­data-explorer/­time-series-analysis
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score1.31
Rank#164  Overall
#14  Time Series DBMS
Score0.09
Rank#362  Overall
#53  Key-value stores
#155  Relational DBMS
Score5.52
Rank#59  Overall
#6  Search engines
Score3.80
Rank#81  Overall
#43  Relational DBMS
Websiteiotdb.apache.orgwww.faircom.com/­products/­faircom-edgeazure.microsoft.com/­en-us/­services/­searchazure.microsoft.com/­services/­data-explorersqrrl.com
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.faircom.com/­docs/­en/­UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.htmllearn.microsoft.com/­en-us/­azure/­searchdocs.microsoft.com/­en-us/­azure/­data-explorer
DeveloperApache Software FoundationFairCom CorporationMicrosoftMicrosoftAmazon infooriginally Sqrrl Data, Inc.
Initial release20181979201520192012
Current release1.1.0, April 2023V3, October 2020V1cloud service with continuous releases
License infoCommercial or Open SourceOpen Source infoApache Version 2.0commercial infoRestricted, free version availablecommercialcommercialcommercial
Cloud-based only infoOnly available as a cloud servicenonoyesyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageJavaANSI C, C++Java
Server operating systemsAll OS with a Java VM (>= 1.8)Android
Linux infoARM, x86
Raspbian
Windows
hostedhostedLinux
Data schemeyesFlexible Schema (defined schema, partial schema, schema free)yesFixed schema with schema-less datatypes (dynamic)schema-free
Typing infopredefined data types such as float or dateyesyes, ANSI Standard SQL Typesyesyes infobool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/­en-us/­azure/­kusto/­query/­scalar-data-typesyes
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
Secondary indexesyesyesyesall fields are automatically indexedyes
SQL infoSupport of SQLSQL-like query languageyes infoANSI SQL queriesnoKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
Native API
ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API
RESTful HTTP APIMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
Accumulo Shell
Java API
JDBC
ODBC
RESTful HTTP API
Thrift
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
C
C#
C++
Java
JavaScript
PHP
Python
VB.Net
C#
Java
JavaScript
Python
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
Actionscript
C infousing GLib
C#
C++
Cocoa
Delphi
Erlang
Go
Haskell
Java
JavaScript
OCaml
Perl
PHP
Python
Ruby
Smalltalk
Server-side scripts infoStored proceduresyesyes info.Net, JavaScript, C/C++noYes, possible languages: KQL, Python, Rno
Triggersyesyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyno
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)File partitioning infoCustomizable business rules for partitioningSharding infoImplicit feature of the cloud serviceSharding infoImplicit feature of the cloud serviceSharding infomaking use of Hadoop
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 infoSynchronous and asynchronous realtime replication based on transaction logsyes infoImplicit feature of the cloud serviceyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.selectable replication factor infomaking use of Hadoop
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknonoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkyes
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
Immediate Consistency
Tunable Consistency
Immediate ConsistencyEventual Consistency
Immediate Consistency
Immediate Consistency infoDocument store kept consistent with combination of global timestamping, row-level transactions, and server-side consistency resolution.
Foreign keys infoReferential integritynoyes infowhen using SQLnonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanoACIDnonoAtomic updates per row, document, or graph entity
Concurrency infoSupport for concurrent manipulation of datayesyes infoacross SQL and NoSQLyesyesyes
Durability infoSupport for making data persistentyesyesyesyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesyesnono
User concepts infoAccess controlyesFine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.yes infousing Azure authenticationAzure Active Directory AuthenticationCell-level Security, Data-Centric Security, Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC)

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More resources
Apache IoTDBFaircom EDGE infoformerly c-treeEDGEMicrosoft Azure AI SearchMicrosoft Azure Data ExplorerSqrrl
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