DBMS > Amazon Redshift vs. BigObject vs. Google BigQuery vs. Graph Engine vs. Riak KV

System Properties Comparison Amazon Redshift vs. BigObject vs. Google BigQuery vs. Graph Engine vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Analytic DBMS for real-time computations and queries

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Graph DBMS
Key-value store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Score	4.10
Rank	#82	Overall
	#9	Key-value stores

Website

aws.amazon.com/redshift

bigobject.io

cloud.google.com/bigquery

www.graphengine.io

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

www.tiot.jp/riak-docs/riak/kv/latest

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Google

Microsoft

OpenSource, formerly Basho Technologies

Initial release

2012

2015

2010

2009

Current release

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

MIT License

Open Source

Apache version 2, commercial enterprise edition

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

.NET and C

Erlang

Server operating systems

hosted

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

.NET

Linux
OS X

Data scheme

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

restricted

yes

restricted

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

user defined functions

in JavaScript

yes

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

links between data sets can be stored

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

yes, using Riak Security

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