DBMS > Alibaba Cloud Table Store vs. Apache IoTDB vs. Microsoft Access vs. Microsoft SQL Server vs. OpenMLDB

System Properties Comparison Alibaba Cloud Table Store vs. Apache IoTDB vs. Microsoft Access vs. Microsoft SQL Server vs. OpenMLDB

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

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Microsofts flagship relational DBMS

An open-source machine learning database that provides a feature platform for training and inference

Primary database model

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Relational 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	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.02
Rank	#367	Overall
	#37	Time Series DBMS

Website

www.alibabacloud.com/product/table-store

iotdb.apache.org

www.microsoft.com/en-us/microsoft-365/access

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

openmldb.ai

Technical documentation

www.alibabacloud.com/help/en/tablestore

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

developer.microsoft.com/en-us/access

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

openmldb.ai/docs/zh/main

Developer

Alibaba

Apache Software Foundation

Microsoft

4 Paradigm Inc.

Initial release

2016

2018

1992

1989

2020

Current release

1.1.0, April 2023

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

2024-2 February 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

Open Source

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

C++, Java, Scala

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Windows

Not a real database server, but making use of DLLs

Linux
Windows

Linux

Data scheme

schema-free

yes

Fixed schema

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

but not compliant to any SQL standard

yes

APIs and other access methods

HTTP API

JDBC
Native API

ADO.NET
DAO
ODBC
OLE DB

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

JDBC
SQLAlchemy

Supported programming languages

Java
Python

C
C#
C++
Go
Java
Python
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

C++
Go
Java
Python
Scala

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

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

Triggers

yes

since Access 2010 using the ACE-engine

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)

none

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

horizontal partitioning

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

none

yes, but depending on the SQL-Server Edition

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

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

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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

a simple user-level security was built in till version Access 2003

fine grained access rights according to SQL-standard

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