DBMS > Apache IoTDB vs. Microsoft Azure Table Storage vs. MySQL vs. SAP HANA vs. Transbase

System Properties Comparison Apache IoTDB vs. Microsoft Azure Table Storage vs. MySQL vs. SAP HANA vs. Transbase

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

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

Widely used open source RDBMS

In-memory, column based data store. Available as appliance or cloud service

A resource-optimized, high-performance, universally applicable RDBMS

Primary database model

Time Series DBMS

Wide column store

Relational DBMS

Key/Value like access via memcached API

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	4.04
Rank	#77	Overall
	#6	Wide column stores

Score	1061.34
Rank	#2	Overall
	#2	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Website

iotdb.apache.org

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

www.mysql.com

www.sap.com/products/hana.html

www.transaction.de/en/products/transbase.html

Technical documentation

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

dev.mysql.com/doc

help.sap.com/hana

www.transaction.de/en/products/transbase/features.html

Developer

Apache Software Foundation

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

SAP

Transaction Software GmbH

Initial release

2018

2012

1995

2010

1987

Current release

1.1.0, April 2023

8.4.0, April 2024

2.0 SPS07 (April 4, 2023), April 2023

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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Implementation language

Java

C and C++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Appliance or cloud-service

FreeBSD
Linux
macOS
Solaris
Windows

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

Server-side scripts

Stored procedures

yes

proprietary syntax

SQLScript, R

yes

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

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

yes

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.

Multi-source replication
Source-replica replication

yes

Source-replica replication

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

not for MyISAM storage engine

yes

Transaction concepts

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

optimistic locking

ACID

not for MyISAM storage engine

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

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

Access rights based on private key authentication or shared access signatures

Users with fine-grained authorization concept

no user groups or roles

yes

fine grained access rights according to SQL-standard

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