DBMS > Amazon DynamoDB vs. Amazon SimpleDB vs. Apache IoTDB vs. Badger vs. BigchainDB

System Properties Comparison Amazon DynamoDB vs. Amazon SimpleDB vs. Apache IoTDB vs. Badger vs. BigchainDB

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

BigchainDB is scalable blockchain database offering decentralization, immutability and native assets

Primary database model

Document store
Key-value store

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

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

Score	1.31
Rank	#164	Overall
	#14	Time Series DBMS

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	0.85
Rank	#208	Overall
	#35	Document stores

Website

aws.amazon.com/dynamodb

aws.amazon.com/simpledb

iotdb.apache.org

github.com/dgraph-io/badger

www.bigchaindb.com

Technical documentation

docs.aws.amazon.com/dynamodb

docs.aws.amazon.com/simpledb

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

godoc.org/github.com/dgraph-io/badger

bigchaindb.readthedocs.io/en/latest

Developer

Amazon

Apache Software Foundation

DGraph Labs

Initial release

2012

2007

2018

2017

2016

Current release

1.1.0, April 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

Open Source

AGPL v3

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

Python

Server operating systems

hosted

All OS with a Java VM (>= 1.8)

BSD
Linux
OS X
Solaris
Windows

Linux

Data scheme

schema-free

yes

schema-free

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.

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

SQL-like query language

APIs and other access methods

RESTful HTTP API

JDBC
Native API

CLI Client
RESTful HTTP API

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

C
C#
C++
Go
Java
Python
Scala

Go
Haskell
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Triggers

yes

by integration with AWS Lambda

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding must be implemented in the application

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

none

Sharding

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

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

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
Immediate Consistency

can be specified for read operations

Eventual Consistency
Strong Consistency with Raft

none

Foreign keys

Referential integrity

Transaction concepts

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

ACID

ACID across one or more tables within a single AWS account and region

Concurrent data updates can be detected by the application

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes,with MongoDB ord RethinkDB

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

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