DBMS > Apache IoTDB vs. Fujitsu Enterprise Postgres vs. MaxDB vs. Microsoft Azure Synapse Analytics vs. TimesTen

System Properties Comparison Apache IoTDB vs. Fujitsu Enterprise Postgres vs. MaxDB vs. Microsoft Azure Synapse Analytics vs. TimesTen

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

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

A robust and reliable RDBMS optimized to run all major SAP solutions

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	2.15
Rank	#115	Overall
	#55	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

iotdb.apache.org

www.postgresql.fastware.com

maxdb.sap.com

azure.microsoft.com/services/synapse-analytics

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

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

www.postgresql.fastware.com/product-manuals

maxdb.sap.com/documentation

docs.microsoft.com/azure/synapse-analytics

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Apache Software Foundation

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

SAP, acquired from Software AG (Adabas-D) in 1997

Microsoft

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2018

1984

2016

1998

Current release

1.1.0, April 2023

Fujitsu Enterprise Postgres 14, January 2022

7.9.10.12, February 2024

Release 22.1

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Limited community edition free

commercial

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)

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

hosted

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

ADO.NET
JDBC
ODBC
WebDAV

ADO.NET
JDBC
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
C#
Java
Perl
PHP
Python

C#
Java
PHP

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

yes

user defined functions

yes

Transact SQL

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

partitioning by range, list and by hash

none

Sharding, horizontal partitioning

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

Source-replica replication

yes

Multi-source replication
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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

fine grained access rights according to SQL-standard

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