DBMS > Amazon Redshift vs. BigObject vs. Blueflood vs. KeyDB vs. RDFox

System Properties Comparison Amazon Redshift vs. BigObject vs. Blueflood vs. KeyDB vs. RDFox

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

Scalable TimeSeries DBMS based on Cassandra

An ultra-fast, open source Key-value store fully compatible with Redis API, modules, and protocols

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Time Series DBMS

Key-value store

Graph DBMS
RDF store

Secondary database models

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.19
Rank	#329	Overall
	#146	Relational DBMS

Score	0.13
Rank	#346	Overall
	#33	Time Series DBMS

Score	0.70
Rank	#229	Overall
	#32	Key-value stores

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Website

aws.amazon.com/redshift

bigobject.io

blueflood.io

github.com/Snapchat/KeyDB
keydb.dev

www.oxfordsemantic.tech

Technical documentation

docs.aws.amazon.com/redshift

docs.bigobject.io

github.com/rax-maas/blueflood/wiki

docs.keydb.dev

docs.oxfordsemantic.tech

Developer

Amazon (based on PostgreSQL)

BigObject, Inc.

Rackspace

EQ Alpha Technology Ltd.

Oxford Semantic Technologies

Initial release

2012

2015

2013

2019

2017

Current release

6.0, Septermber 2022

License

Commercial or Open Source

commercial

free community edition available

Open Source

Apache 2.0

Open Source

BSD-3

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

C++

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)

Linux
OS X

Linux

Linux
macOS
Windows

Data scheme

yes

predefined scheme

schema-free

yes

RDF schemas

Typing

predefined data types such as float or date

yes

partial

Supported data types are strings, hashes, lists, sets and sorted sets, bit arrays, hyperloglogs and geospatial indexes

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

by using the Redis Search module

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

fluentd
ODBC
RESTful HTTP API

HTTP REST

Proprietary protocol

RESP - REdis Serialization Protoco

RESTful HTTP API
SPARQL 1.1

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Clojure
Crystal
D
Dart
Elixir
Erlang
Fancy
Go
Haskell
Haxe
Java
JavaScript (Node.js)
Lisp
Lua
MatLab
Objective-C
OCaml
Pascal
Perl
PHP
Prolog
Pure Data
Python
R
Rebol
Ruby
Rust
Scala
Scheme
Smalltalk
Swift
Tcl
Visual Basic

C
Java

Server-side scripts

Stored procedures

user defined functions

in Python

Lua

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

selectable replication factor

based on Cassandra

Multi-source replication
Source-replica replication

replication via a shared file system

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Strong eventual consistency with CRDTs

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

automatically between fact table and dimension tables

Transaction concepts

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

ACID

Optimistic locking, atomic execution of commands blocks and scripts

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

Configurable mechanisms for persistency via snapshots and/or operations logs

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

simple password-based access control and ACL

Roles, resources, and access types

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