DBMS > Amazon Redshift vs. Blazegraph vs. Citus vs. EXASOL vs. HBase

System Properties Comparison Amazon Redshift vs. Blazegraph vs. Citus vs. EXASOL vs. HBase

Editorial information provided by DB-Engines

Name

Amazon has acquired Blazegraph's domain and (probably) product. It is said that Amazon Neptune is based on Blazegraph.

Description

Large scale data warehouse service for use with business intelligence tools

High-performance graph database supporting Semantic Web (RDF/SPARQL) and Graph Database (tinkerpop3, blueprints, vertex-centric) APIs with scale-out and High Availability.

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

Primary database model

Secondary database models

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.75
Rank	#219	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	1.99
Rank	#124	Overall
	#58	Relational DBMS

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

wiki.blazegraph.com

docs.citusdata.com

www.exasol.com/resources

hbase.apache.org/book.html

Developer

Amazon (based on PostgreSQL)

Blazegraph

Exasol

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Initial release

2012

2006

2010

2000

2008

Current release

2.1.5, March 2019

8.1, December 2018

2.3.4, January 2021

License

Commercial or Open Source

commercial

Open Source

extended commercial license available

Open Source

AGPL, commercial license also available

commercial

Open Source

Apache version 2

Cloud-based only

Only available as a cloud service

yes

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Implementation language

Java

Server operating systems

hosted

Linux
OS X
Windows

Linux

Linux
Unix
Windows

using Cygwin

Data scheme

yes

schema-free

yes

schema-free, schema definition possible

Typing

predefined data types such as float or date

yes

RDF literal types

yes

options to bring your own types, AVRO

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.

yes

specific XML type available, but no XML query functionality

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

SPARQL is used as query language

yes

standard, with numerous extensions

yes

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.Net
JDBC
ODBC
WebSocket

Java API
RESTful HTTP API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

Java
Lua
Python
R

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

yes

Coprocessors in Java

Triggers

yes

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

other methods possible by using 3rd party extensions

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Immediate Consistency or Eventual Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Relationships in Graphs

yes

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

Security and Authentication via Web Application Container (Tomcat, Jetty)

fine grained access rights according to SQL-standard

Access rights for users, groups and roles according to SQL-standard

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

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