DBMS > Amazon Redshift vs. ArcadeDB vs. HBase vs. JaguarDB vs. Riak KV

System Properties Comparison Amazon Redshift vs. ArcadeDB vs. HBase vs. JaguarDB vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Fast and scalable multi-model DBMS, originally forked from OrientDB but most of the code has been rewritten

Wide-column store based on Apache Hadoop and on concepts of BigTable

Performant, highly scalable DBMS for AI and IoT applications

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Wide column store

Key-value store
Vector DBMS

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	0.02
Rank	#366	Overall
	#50	Document stores
	#38	Graph DBMS
	#53	Key-value stores
	#36	Time Series DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.00
Rank	#383	Overall
	#60	Key-value stores
	#13	Vector DBMS

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

Website

aws.amazon.com/redshift

arcadedb.com

hbase.apache.org

www.jaguardb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.arcadedb.com

hbase.apache.org/book.html

www.jaguardb.com/support.html

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

Developer

Amazon (based on PostgreSQL)

Arcade Data

Apache Software Foundation

Apache top-level project, originally developed by Powerset

DataJaguar, Inc.

OpenSource, formerly Basho Technologies

Initial release

2012

2021

2008

2015

2009

Current release

September 2021

2.3.4, January 2021

3.3 July 2023

3.2.0, December 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache version 2

Open Source

GPL V3.0

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

Java

C++

the server part. Clients available in other languages

Erlang

Server operating systems

hosted

All OS with a Java VM

Linux
Unix
Windows

using Cygwin

Linux

Linux
OS X

Data scheme

yes

schema-free

schema-free, schema definition possible

yes

schema-free

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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 query language, no joins

A subset of ANSI SQL is implemented

but no views, foreign keys, triggers

APIs and other access methods

JDBC
ODBC

JDBC
MongoDB API
OpenCypher
PostgreSQL wire protocol
Redis API
RESTful HTTP/JSON API
TinkerPop Gremlin

Java API
RESTful HTTP API
Thrift

JDBC
ODBC

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

Java

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

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

yes

Coprocessors in Java

Erlang

Triggers

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Multi-source replication
Source-replica replication

Multi-source replication

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

Immediate Consistency or Eventual Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationship in graphs

links between data sets can be stored

Transaction concepts

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

ACID

Single row ACID (across millions of columns)

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

rights management via user accounts

yes, using Riak Security

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.