DBMS > Apache IoTDB vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Newts

System Properties Comparison Apache IoTDB vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Newts

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

Common in IBM host environments, 2 different versions for host and Windows/Linux

Fully managed big data interactive analytics platform

Time Series DBMS based on Cassandra

Primary database model

Time Series DBMS

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

column oriented

Time Series DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

Document store

If 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

this 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

support 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

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

iotdb.apache.org

www.ibm.com/products/db2

azure.microsoft.com/services/data-explorer

opennms.github.io/newts

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

www.ibm.com/docs/en/db2

docs.microsoft.com/en-us/azure/data-explorer

github.com/OpenNMS/newts/wiki

Developer

Apache Software Foundation

IBM

Microsoft

OpenNMS Group

Initial release

2018

1983

host version

2019

2014

Current release

1.1.0, April 2023

12.1, October 2016

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

free version is available

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C and C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Linux
OS X
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

Typing

predefined data types such as float or date

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

yes

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

HTTP REST
Java API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Java

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

Triggers

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

Sharding

only with Windows/Unix/Linux Version

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

yes

with separate tools (MQ, InfoSphere)

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Eventual Consistency
Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

User concepts

Access control

yes

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

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