DBMS > Amazon Redshift vs. Datomic vs. HugeGraph vs. Kyligence Enterprise

System Properties Comparison Amazon Redshift vs. Datomic vs. HugeGraph vs. Kyligence Enterprise

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Editorial information provided by DB-Engines

Name

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

A fast-speed and highly-scalable Graph DBMS

A distributed analytics engine for big data, built on top of Apache Kylin

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Website

aws.amazon.com/redshift

www.datomic.com

github.com/hugegraph
hugegraph.apache.org

kyligence.io/kyligence-enterprise

Technical documentation

docs.aws.amazon.com/redshift

docs.datomic.com

hugegraph.apache.org/docs

Developer

Amazon (based on PostgreSQL)

Cognitect

Baidu

Kyligence, Inc.

Initial release

2012

2018

2016

Current release

1.0.6735, June 2023

0.9

License

Commercial or Open Source

commercial

limited edition free

Open Source

Apache Version 2.0

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, Clojure

Java

Server operating systems

hosted

All OS with a Java VM

Linux
macOS
Unix

Linux

Data scheme

yes

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Java API
RESTful HTTP API
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Clojure
Java

Groovy
Java
Python

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

asynchronous Gremlin script jobs

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

yes

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

edges in graph

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)

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

Users, roles and permissions

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