DBMS > Apache IoTDB vs. Microsoft Azure Synapse Analytics vs. NuoDB vs. OceanBase vs. SQLite

System Properties Comparison Apache IoTDB vs. Microsoft Azure Synapse Analytics vs. NuoDB vs. OceanBase vs. SQLite

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

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

NuoDB is a webscale distributed database that supports SQL and ACID transactions

A distributed, high available RDBMS compatible with Oracle and MySQL

Widely used embeddable, in-process RDBMS

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Wide column store

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	19.93
Rank	#31	Overall
	#19	Relational DBMS

Score	0.94
Rank	#197	Overall
	#92	Relational DBMS

Score	1.57
Rank	#149	Overall
	#69	Relational DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

iotdb.apache.org

azure.microsoft.com/services/synapse-analytics

www.3ds.com/nuodb-distributed-sql-database

en.oceanbase.com

www.sqlite.org

Technical documentation

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

docs.microsoft.com/azure/synapse-analytics

doc.nuodb.com

en.oceanbase.com/docs/oceanbase-database

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

OceanBase

previously Alibaba and Ant Group

Dwayne Richard Hipp

Initial release

2018

2016

2013

2010

2000

Current release

1.1.0, April 2023

4.3.0, April 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

limited edition free

Open Source

Commercial license available

Open Source

Public Domain

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++

Server operating systems

All OS with a Java VM (>= 1.8)

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

Linux

server-less

Data scheme

yes

dynamic column types

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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-92 is not fully supported

APIs and other access methods

JDBC
Native API

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)
Proprietary native API
Table API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

C#
Java
PHP

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

Ada

in MySQL-compatible model
C

in Oracle- and MySQL- compatible models
C++

in Oracle- and MySQL- compatible models
D

in MySQL-compatible model
Delphi

in MySQL-compatible model
Eiffel

in MySQL-compatible model
Erlang

in MySQL-compatible model
Haskell

in MySQL-compatible model
Java

in Oracle- and MySQL- compatible models
JavaScript (Node.js)

in MySQL-compatible model
Objective-C

in MySQL-compatible model
OCaml

in MySQL-compatible model
Perl

in MySQL-compatible model
PHP

in MySQL-compatible model
Python

in MySQL-compatible model
Ruby

in MySQL-compatible model
Scheme

in MySQL-compatible model
Tcl

in MySQL-compatible model

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Server-side scripts

Stored procedures

yes

Transact SQL

Java, SQL

PL/SQL in oracle-compatible mode, MySQL Stored Procedure in mysql-compatible mode

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

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

Sharding, horizontal partitioning

data is dynamically stored/cached on the nodes where it is read/written

horizontal partitioning (by hash, key, range, range columns, list, and list columns)

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

yes

Managed transparently by NuoDB

Multi-source replication using Paxos

none

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

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

tunable commit protocol

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC

yes

via file-system locks

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

Temporary table

yes

User concepts

Access control

yes

Standard SQL roles/ privileges, Administrative Users

Access rights for users, groups and roles according to SQL-standard

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