DBMS > Amazon SimpleDB vs. CockroachDB vs. Google BigQuery vs. IBM Cloudant vs. JaguarDB

System Properties Comparison Amazon SimpleDB vs. CockroachDB vs. Google BigQuery vs. IBM Cloudant vs. JaguarDB

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

CockroachDB is a distributed database architected for modern cloud applications. It is wire compatible with PostgreSQL and backed by a Key-Value Store, which is either RocksDB or a purpose-built derivative, called Pebble.

Large scale data warehouse service with append-only tables

Database as a Service offering based on Apache CouchDB

Performant, highly scalable DBMS for AI and IoT applications

Primary database model

Key-value store

Relational DBMS

Document store

Key-value store
Vector DBMS

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

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

Score	5.73
Rank	#58	Overall
	#34	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.75
Rank	#104	Overall
	#19	Document stores

Score	0.06
Rank	#381	Overall
	#59	Key-value stores
	#13	Vector DBMS

Website

aws.amazon.com/simpledb

www.cockroachlabs.com

cloud.google.com/bigquery

www.ibm.com/products/cloudant

www.jaguardb.com

Technical documentation

docs.aws.amazon.com/simpledb

www.cockroachlabs.com/docs

cloud.google.com/bigquery/docs

cloud.ibm.com/docs/Cloudant

www.jaguardb.com/support.html

Developer

Amazon

Cockroach Labs

Google

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

DataJaguar, Inc.

Initial release

2007

2015

2010

2015

Current release

24.1.0, May 2024

3.3 July 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0, commercial license available

commercial

Open Source

GPL V3.0

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

Erlang

C++

the server part. Clients available in other languages

Server operating systems

hosted

Linux
macOS
Windows

hosted

Linux

Data scheme

schema-free

dynamic schema

yes

schema-free

yes

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

yes, wire compatible with PostgreSQL

yes

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

RESTful HTTP API

JDBC

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

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

C#
C++
Clojure
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby
Rust

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Objective-C
PHP
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

View functions (Map-Reduce) in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

horizontal partitioning (by key range)

all tables are translated to an ordered KV store and then broken down into 64MB ranges, which are then used as replicas in RAFT

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using RAFT

Multi-source replication
Source-replica replication

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Eventual 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

ACID

Since BigQuery is designed for querying data

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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.

User concepts

Access control

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

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users can be defined per database

rights management via user accounts

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