DBMS > Amazon Redshift vs. Datomic vs. NSDb vs. OrientDB vs. Yaacomo

System Properties Comparison Amazon Redshift vs. Datomic vs. NSDb vs. OrientDB vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Large scale data warehouse service for use with business intelligence tools

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

Multi-model DBMS (Document, Graph, Key/Value)

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store

Relational DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Website

aws.amazon.com/redshift

www.datomic.com

nsdb.io

orientdb.org

yaacomo.com

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

nsdb.io/Architecture

www.orientdb.com/docs/last/index.html

Developer

Amazon (based on PostgreSQL)

Cognitect

OrientDB LTD; CallidusCloud; SAP

Q2WEB GmbH

Initial release

2012

2017

2010

2009

Current release

1.0.6735, June 2023

3.2.29, March 2024

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2.0

Open Source

Apache version 2

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

Java, Clojure

Java, Scala

Java

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

All OS with a Java JDK (>= JDK 6)

Android
Linux
Windows

Data scheme

yes

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SQL-like query language

SQL-like query language, no joins

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

gRPC
HTTP REST
WebSocket

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

Java
Scala

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Java, Javascript

Triggers

By using transaction functions

Hooks

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

could be achieved with distributed queries

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

yes

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

Using Apache Lucene

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

fine grained access rights according to SQL-standard

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