DBMS > Apache IoTDB vs. Oracle Rdb vs. Snowflake vs. TDSQL for MySQL vs. Teradata Aster

System Properties Comparison Apache IoTDB vs. Oracle Rdb vs. Snowflake vs. TDSQL for MySQL vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

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

Cloud-based data warehousing service for structured and semi-structured data

A high-performance distributed database management system with features such as automatic sharding, intelligent operation and maintenance, elastic scalability without downtime, and enterprise-grade security. It is highly compatible with MySQL.

Platform for big data analytics on multistructured data sources and types

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	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	0.85
Rank	#205	Overall
	#95	Relational DBMS

Website

iotdb.apache.org

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

www.snowflake.com

www.tencentcloud.com/products/dcdb

Technical documentation

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

www.oracle.com/database/technologies/related/rdb-doc.html

docs.snowflake.net/manuals/index.html

www.tencentcloud.com/document/product/1042

Developer

Apache Software Foundation

Oracle, originally developed by Digital Equipment Corporation (DEC)

Snowflake Computing Inc.

Tencent

Teradata

Initial release

2018

1984

2014

2013

2005

Current release

1.1.0, April 2023

7.4.1.1, 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

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

Server operating systems

All OS with a Java VM (>= 1.8)

HP Open VMS

hosted

Linux

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

JDBC
Native API

CLI Client
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

JavaScript (Node.js)
Python

C
C#
C++
Java
PHP
Python
Ruby

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

user defined functions

yes

R packages

Triggers

yes

similar concept for controling cloud resources

yes

Partitioning methods

Methods for storing different data on different nodes

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

yes

Automatic sharding

Sharding

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

Multi-source replication
Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

yes

SQL Map-Reduce Framework

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

Transaction concepts

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

yes, on a single node

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

User concepts

Access control

yes

Users with fine-grained authorization concept, user roles and pluggable authentication

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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