DBMS > Amazon SimpleDB vs. Brytlyt vs. CockroachDB vs. etcd vs. Google BigQuery

System Properties Comparison Amazon SimpleDB vs. Brytlyt vs. CockroachDB vs. etcd vs. Google BigQuery

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

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

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.

A distributed reliable key-value store

Large scale data warehouse service with append-only tables

Primary database model

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	6.15
Rank	#55	Overall
	#33	Relational DBMS

Score	7.25
Rank	#54	Overall
	#5	Key-value stores

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Website

aws.amazon.com/simpledb

brytlyt.io

www.cockroachlabs.com

etcd.io
github.com/etcd-io/etcd

cloud.google.com/bigquery

Technical documentation

docs.aws.amazon.com/simpledb

docs.brytlyt.io

www.cockroachlabs.com/docs

etcd.io/docs
github.com/etcd-io/etcd/tree/master/Documentation

cloud.google.com/bigquery/docs

Developer

Amazon

Brytlyt

Cockroach Labs

Google

Initial release

2007

2016

2015

2010

Current release

5.0, August 2023

23.1.1, May 2023

3.4, August 2019

License

Commercial or Open Source

commercial

Open Source

Apache 2.0, commercial license available

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

C, C++ and CUDA

Server operating systems

hosted

Linux
OS X
Windows

Linux
macOS
Windows

FreeBSD
Linux
Windows

experimental

hosted

Data scheme

schema-free

yes

dynamic schema

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

yes

yes, wire compatible with PostgreSQL

yes

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

JDBC

gRPC
JSON over HTTP

RESTful HTTP/JSON API

Supported programming languages

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

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

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

.Net
C
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Rust
Scala
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

in JavaScript

Triggers

yes

yes, watching key changes

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication using RAFT

Using Raft consensus algorithm to ensure data replication with strong consistency among multiple replicas.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

ACID

Since BigQuery is designed for querying data

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.

User concepts

Access control

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

fine grained access rights according to SQL-standard

Role-based access control

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

Google Cloud Identity & Access Management (IAM)

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