DBMS > Apache IoTDB vs. IBM Db2 warehouse vs. Microsoft Azure Table Storage vs. SpatiaLite

System Properties Comparison Apache IoTDB vs. IBM Db2 warehouse vs. Microsoft Azure Table Storage vs. SpatiaLite

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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

Cloud-based data warehousing service

A Wide Column Store for rapid development using massive semi-structured datasets

Spatial extension of SQLite

Primary database model

Secondary database models

Relational DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.30
Rank	#164	Overall
	#75	Relational DBMS

Score	4.48
Rank	#75	Overall
	#6	Wide column stores

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

iotdb.apache.org

www.ibm.com/products/db2/warehouse

azure.microsoft.com/en-us/services/storage/tables

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

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

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Apache Software Foundation

IBM

Microsoft

Alessandro Furieri

Initial release

2018

2014

2012

2008

Current release

1.1.0, April 2023

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

server-less

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

.NET Client API
JDBC
ODBC
OLE DB

RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

PL/SQL, SQL PL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

Implicit feature of the cloud service

none

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

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

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

optimistic locking

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

Access rights based on private key authentication or shared access signatures

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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