DBMS > Apache IoTDB vs. Immudb vs. Microsoft Azure SQL Database vs. SAP HANA

System Properties Comparison Apache IoTDB vs. Immudb vs. Microsoft Azure SQL Database vs. SAP HANA

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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

An open source immutable (append-only) database with cryptographic verification which makes it tamper-resistant and fully auditable.

Database as a Service offering with high compatibility to Microsoft SQL Server

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

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS
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	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.24
Rank	#305	Overall
	#43	Key-value stores

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Website

iotdb.apache.org

github.com/codenotary/immudb
immudb.io

azure.microsoft.com/en-us/products/azure-sql/database

www.sap.com/products/hana.html

Technical documentation

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

docs.immudb.io

docs.microsoft.com/en-us/azure/azure-sql

help.sap.com/hana

Developer

Apache Software Foundation

Codenotary

Microsoft

SAP

Initial release

2018

2020

2010

Current release

1.1.0, April 2023

1.2.3, April 2022

V12

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

Open Source

Apache Version 2.0

Open Source

Apache Version 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

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)

BSD
Linux
macOS
Solaris
Windows
z/OS

hosted

Appliance or cloud-service

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

SQL-like syntax

yes

APIs and other access methods

JDBC
Native API

gRPC protocol
PostgreSQL wire protocol

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

yes

Transact SQL

SQLScript, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding

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, with always 3 replicas available

yes

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

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

yes

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