DBMS > Amazon Redshift vs. AnzoGraph DB vs. Apache Druid vs. HyperSQL

System Properties Comparison Amazon Redshift vs. AnzoGraph DB vs. Apache Druid vs. HyperSQL

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Scalable graph database built for online analytics and data harmonization with MPP scaling, high-performance analytical algorithms and reasoning, and virtualization

Open-source analytics data store designed for sub-second OLAP queries on high dimensionality and high cardinality data

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational DBMS
Time Series DBMS

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	0.29
Rank	#303	Overall
	#25	Graph DBMS
	#14	RDF stores

Score	3.25
Rank	#90	Overall
	#47	Relational DBMS
	#7	Time Series DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Website

aws.amazon.com/redshift

cambridgesemantics.com/anzograph

druid.apache.org

hsqldb.org

Technical documentation

docs.aws.amazon.com/redshift

docs.cambridgesemantics.com/anzograph/userdoc/home.htm

druid.apache.org/docs/latest/design

hsqldb.org/web/hsqlDocsFrame.html

Developer

Amazon (based on PostgreSQL)

Cambridge Semantics

Apache Software Foundation and contributors

Initial release

2012

2018

2012

2001

Current release

2.3, January 2021

29.0.1, April 2024

2.7.2, June 2023

License

Commercial or Open Source

commercial

free trial version available

Open Source

Apache license v2

Open Source

based on BSD license

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

Server operating systems

hosted

Linux

Linux
OS X
Unix

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

Data scheme

yes

Schema-free and OWL/RDFS-schema support

yes

schema-less columns are supported

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

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL and SPARQL* as primary query language. Cypher preview.

SQL for querying

yes

APIs and other access methods

JDBC
ODBC

Apache Mule
gRPC
JDBC
Kafka
OData access for BI tools
OpenCypher
RESTful HTTP API
SPARQL

JDBC
RESTful HTTP/JSON API

HTTP API

JDBC via HTTP
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
Python

Clojure
JavaScript
PHP
Python
R
Ruby
Scala

All languages supporting JDBC/ODBC
Java

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

Java, SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Automatic sharding

Sharding

manual/auto, time-based

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication in MPP-Cluster

yes, via HDFS, S3 or other storage engines

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Kerberos/HDFS data loading

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in MPP-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

not needed 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

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 rights for users and roles

RBAC using LDAP or Druid internals for users and groups for read/write by datasource and system

fine grained access rights according to SQL-standard

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