DBMS > Amazon SimpleDB vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. STSdb vs. XTDB

System Properties Comparison Amazon SimpleDB vs. Google BigQuery vs. Microsoft Azure Cosmos DB vs. STSdb vs. XTDB

Editorial information provided by DB-Engines

Name

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

Large scale data warehouse service with append-only tables

Globally distributed, horizontally scalable, multi-model database service

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Key-value store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Document store

Secondary database models

Spatial DBMS

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

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.18
Rank	#332	Overall
	#46	Document stores

Website

aws.amazon.com/simpledb

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

github.com/STSSoft/STSdb4

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/simpledb

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

www.xtdb.com/docs

Developer

Amazon

Google

Microsoft

STS Soft SC

Juxt Ltd.

Initial release

2007

2010

2014

2011

2019

Current release

4.0.8, September 2015

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

GPLv2, commercial license available

Open Source

MIT License

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

Clojure

Server operating systems

hosted

Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

schema-free

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

primitive types and user defined types (classes)

yes, extensible-data-notation format

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

limited SQL, making use of Apache Calcite

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

.NET Client API

HTTP REST
JDBC

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C#
Java

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

none

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Concurrent data updates can be detected by the application

Since BigQuery is designed for querying data

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

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