DBMS > Alibaba Cloud Table Store vs. Apache IoTDB vs. Microsoft SQL Server vs. SAP IQ vs. Transbase

System Properties Comparison Alibaba Cloud Table Store vs. Apache IoTDB vs. Microsoft SQL Server vs. SAP IQ vs. Transbase

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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

Microsofts flagship relational DBMS

Columnar RDBMS optimized for Big Data analytics

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

Primary database model

column-oriented

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	2.75
Rank	#104	Overall
	#53	Relational DBMS

Score	0.11
Rank	#341	Overall
	#150	Relational DBMS

Website

www.alibabacloud.com/product/table-store

iotdb.apache.org

www.microsoft.com/en-us/sql-server

www.sap.com/products/technology-platform/sybase-iq-big-data-management.html

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

Technical documentation

www.alibabacloud.com/help/en/tablestore

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

learn.microsoft.com/en-US/sql/sql-server

help.sap.com/docs/SAP_IQ

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

Developer

Alibaba

Apache Software Foundation

Microsoft

SAP, formerly Sybase

Transaction Software GmbH

Initial release

2016

2018

1989

1994

1987

Current release

1.1.0, April 2023

SQL Server 2022, November 2022

16.1 SPS04, April 2019

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

restricted free version is available

commercial

free development license

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

C and C++

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Linux
Windows

AIX
HP-UX
Linux
Solaris
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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

APIs and other access methods

HTTP API

JDBC
Native API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

ADO.NET
JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

Java
Python

C
C#
C++
Go
Java
Python
Scala

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

C
C#
C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

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

tables can be distributed across several files (horizontal partitioning); sharding through federation

shared disk or shared nothing architectures with SAP IQ Multiplexer

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

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

yes, but depending on the SQL-Server Edition

SAP/Sybase Replication Server

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Atomic single-row operations

ACID

yes

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

Access rights based on subaccounts and tokens

yes

fine grained access rights according to SQL-standard

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