DBMS > Apache IoTDB vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Spark SQL vs. Tibero

System Properties Comparison Apache IoTDB vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. Spark SQL vs. Tibero

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

Microsofts flagship relational DBMS

Spark SQL is a component on top of 'Spark Core' for structured data processing

A secure RDBMS, designed for easy portability from Oracle

Primary database model

Time Series DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
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	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	1.59
Rank	#148	Overall
	#68	Relational DBMS

Website

iotdb.apache.org

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

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

spark.apache.org/sql

us.tmaxsoft.com/products/tibero

Technical documentation

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

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

spark.apache.org/docs/latest/sql-programming-guide.html

technet.tmaxsoft.com/upload/download/online/tibero/pver-20150504-000002/index.html

Developer

Apache Software Foundation

Microsoft

Apache Software Foundation

TmaxSoft

Initial release

2018

2012

1989

2014

2003

Current release

1.1.0, April 2023

SQL Server 2022, November 2022

3.5.0 ( 2.13), September 2023

6, April 2015

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

restricted free version is available

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

C++

Scala

C and Assembler

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Linux
Windows

Linux
OS X
Windows

AIX
HP-UX
Linux
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

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
Native API

RESTful HTTP API

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

JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Tibero CLI

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

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

Java
Python
R
Scala

C
C#
C++
Cobol
Java
Objective-C
Perl
PHP
Python
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

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

Persistent Stored Procedure (PSM)

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

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

yes, utilizing Spark Core

horizontal partitioning

by range, hash, list or composite

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.

yes, but depending on the SQL-Server Edition

none

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

optimistic locking

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

planned for next version

User concepts

Access control

yes

Access rights based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

fine grained access rights according to SQL-standard (SQL 92, SQL 99)

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