DBMS > Amazon Redshift vs. Blazegraph vs. EXASOL vs. LeanXcale

System Properties Comparison Amazon Redshift vs. Blazegraph vs. EXASOL vs. LeanXcale

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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.

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

A highly scalable full ACID SQL database with fast NoSQL data ingestion and GIS capabilities

Primary database model

Relational DBMS

Graph DBMS
RDF store

Relational 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	0.77
Rank	#222	Overall
	#20	Graph DBMS
	#8	RDF stores

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	0.35
Rank	#283	Overall
	#41	Key-value stores
	#128	Relational DBMS

Website

aws.amazon.com/redshift

blazegraph.com

www.exasol.com

www.leanxcale.com

Technical documentation

docs.aws.amazon.com/redshift

wiki.blazegraph.com

www.exasol.com/resources

Developer

Amazon (based on PostgreSQL)

Blazegraph

Exasol

LeanXcale

Initial release

2012

2006

2000

2015

Current release

2.1.5, March 2019

License

Commercial or Open Source

commercial

Open Source

extended commercial license available

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

Server operating systems

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

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 is used as query language

yes

through Apache Derby

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

.Net
JDBC
ODBC
WebSocket

JDBC
Kafka Connector
ODBC
proprietary key/value interface
Spark Connector

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Java
Lua
Python
R

C
Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate 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

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)

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

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