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DBMS > Apache IoTDB vs. Cachelot.io vs. Microsoft Azure Data Explorer vs. WakandaDB

System Properties Comparison Apache IoTDB vs. Cachelot.io vs. Microsoft Azure Data Explorer vs. WakandaDB

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Editorial information provided by DB-Engines
NameApache IoTDB  Xexclude from comparisonCachelot.io  Xexclude from comparisonMicrosoft Azure Data Explorer  Xexclude from comparisonWakandaDB  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 FlinkIn-memory caching systemFully managed big data interactive analytics platformWakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data
Primary database modelTime Series DBMSKey-value storeRelational DBMS infocolumn orientedObject oriented DBMS
Secondary database modelsDocument 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
Score0.04
Rank#388  Overall
#62  Key-value stores
Score3.80
Rank#81  Overall
#43  Relational DBMS
Score0.10
Rank#356  Overall
#16  Object oriented DBMS
Websiteiotdb.apache.orgcachelot.ioazure.microsoft.com/­services/­data-explorerwakanda.github.io
Technical documentationiotdb.apache.org/­UserGuide/­Master/­QuickStart/­QuickStart.htmldocs.microsoft.com/­en-us/­azure/­data-explorerwakanda.github.io/­doc
DeveloperApache Software FoundationMicrosoftWakanda SAS
Initial release2018201520192012
Current release1.1.0, April 2023cloud service with continuous releases2.7.0 (April 29, 2019), April 2019
License infoCommercial or Open SourceOpen Source infoApache Version 2.0Open Source infoSimplified BSD LicensecommercialOpen Source infoAGPLv3, extended commercial license available
Cloud-based only infoOnly available as a cloud servicenonoyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

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Implementation languageJavaC++C++, JavaScript
Server operating systemsAll OS with a Java VM (>= 1.8)FreeBSD
Linux
OS X
hostedLinux
OS X
Windows
Data schemeyesschema-freeFixed schema with schema-less datatypes (dynamic)yes
Typing infopredefined data types such as float or dateyesnoyes 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.nonoyesno
Secondary indexesyesnoall fields are automatically indexed
SQL infoSupport of SQLSQL-like query languagenoKusto Query Language (KQL), SQL subsetno
APIs and other access methodsJDBC
Native API
Memcached protocolMicrosoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API
RESTful HTTP API
Supported programming languagesC
C#
C++
Go
Java
Python
Scala
.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
OCaml
Perl
PHP
Python
Ruby
.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R
JavaScript
Server-side scripts infoStored proceduresyesnoYes, possible languages: KQL, Python, Ryes
Triggersyesnoyes infosee docs.microsoft.com/­en-us/­azure/­kusto/­management/­updatepolicyyes
Partitioning methods infoMethods for storing different data on different nodeshorizontal partitioning (by time range) + vertical partitioning (by deviceId)noneSharding infoImplicit feature of the cloud servicenone
Replication methods infoMethods for redundantly storing data on multiple nodesselectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicasnoneyes infoImplicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.none
MapReduce infoOffers an API for user-defined Map/Reduce methodsIntegration with Hadoop and SparknoSpark connector (open source): github.com/­Azure/­azure-kusto-sparkno
Consistency concepts infoMethods to ensure consistency in a distributed systemEventual Consistency
Strong Consistency with Raft
noneEventual Consistency
Immediate Consistency
Immediate Consistency
Foreign keys infoReferential integritynonono
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datanononoACID
Concurrency infoSupport for concurrent manipulation of datayesyesyesyes
Durability infoSupport for making data persistentyesnoyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.yesnonono
User concepts infoAccess controlyesnoAzure Active Directory Authenticationyes

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More resources
Apache IoTDBCachelot.ioMicrosoft Azure Data ExplorerWakandaDB
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