DBMS > Amazon Redshift vs. ArcadeDB vs. EventStoreDB vs. Heroic

System Properties Comparison Amazon Redshift vs. ArcadeDB vs. EventStoreDB vs. Heroic

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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

Industrial-strength, open-source database solution built from the ground up for event sourcing.

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Time Series DBMS

in next version

Event Store

Time Series DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.10
Rank	#358	Overall
	#48	Document stores
	#38	Graph DBMS
	#52	Key-value stores
	#35	Time Series DBMS

Score	1.19
Rank	#173	Overall
	#1	Event Stores

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Website

aws.amazon.com/redshift

arcadedb.com

www.eventstore.com

github.com/spotify/heroic

Technical documentation

docs.aws.amazon.com/redshift

docs.arcadedb.com

developers.eventstore.com

spotify.github.io/heroic

Developer

Amazon (based on PostgreSQL)

Arcade Data

Event Store Limited

Spotify

Initial release

2012

2021

2012

2014

Current release

September 2021

21.2, February 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Apache 2.0

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

Java

Server operating systems

hosted

All OS with a Java VM

Linux
Windows

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

via Elasticsearch

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language, no joins

APIs and other access methods

JDBC
ODBC

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Java

Server-side scripts

Stored procedures

user defined functions

in Python

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

relationship in graphs

Transaction concepts

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

ACID

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

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