DBMS > Amazon SimpleDB vs. Apache IoTDB vs. Atos Standard Common Repository vs. Newts vs. Snowflake

System Properties Comparison Amazon SimpleDB vs. Apache IoTDB vs. Atos Standard Common Repository vs. Newts vs. Snowflake

Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

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

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

Time Series DBMS based on Cassandra

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

Primary database model

Key-value store

Time Series DBMS

Document store
Key-value store

Time Series DBMS

Relational DBMS

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

Score	1.88
Rank	#133	Overall
	#23	Key-value stores

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.07
Rank	#375	Overall
	#41	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

aws.amazon.com/simpledb

iotdb.apache.org

atos.net/en/convergence-creators/portfolio/standard-common-repository

opennms.github.io/newts

www.snowflake.com

Technical documentation

docs.aws.amazon.com/simpledb

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

github.com/OpenNMS/newts/wiki

docs.snowflake.net/manuals/index.html

Developer

Amazon

Apache Software Foundation

Atos Convergence Creators

OpenNMS Group

Snowflake Computing Inc.

Initial release

2007

2018

2016

2014

Current release

1.1.0, April 2023

1703

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

Open Source

Apache 2.0

commercial

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

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

Linux

Linux
OS X
Windows

hosted

Data scheme

schema-free

yes

Schema and schema-less with LDAP views

schema-free

yes

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

Typing

predefined data types such as float or date

yes

optional

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

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP API

JDBC
Native API

LDAP

HTTP REST
Java API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

C
C#
C++
Go
Java
Python
Scala

All languages with LDAP bindings

Java

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

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

Sharding

cell division

Sharding

based on Cassandra

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

selectable replication factor

based on Cassandra

yes

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
Immediate Consistency

can be specified for read operations

Eventual Consistency
Strong Consistency with Raft

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

Atomic execution of specific operations

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

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

yes

LDAP bind authentication

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

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