DBMS > Blazegraph vs. HEAVY.AI vs. Linter vs. SiriDB

System Properties Comparison Blazegraph vs. HEAVY.AI vs. Linter vs. SiriDB

Please select another system to include it in the comparison.

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

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

A high performance, column-oriented RDBMS, specifically developed to harness the massive parallelism of modern CPU and GPU hardware

RDBMS for high security requirements

Open Source Time Series DBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.74
Rank	#217	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	1.41
Rank	#153	Overall
	#71	Relational DBMS

Score	0.03
Rank	#368	Overall
	#156	Relational DBMS

Score	0.00
Rank	#385	Overall
	#40	Time Series DBMS

Website

blazegraph.com

github.com/heavyai/heavydb
www.heavy.ai

linter.ru

siridb.com

Technical documentation

wiki.blazegraph.com

docs.heavy.ai

docs.siridb.com

Developer

Blazegraph

HEAVY.AI, Inc.

relex.ru

Cesbit

Initial release

2006

2016

1990

2017

Current release

2.1.5, March 2019

5.10, January 2022

License

Commercial or Open Source

Open Source

extended commercial license available

Open Source

Apache Version 2; enterprise edition available

commercial

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

Java

C++ and CUDA

C and C++

Server operating systems

Linux
OS X
Windows

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

RDF literal types

yes

Numeric data

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

yes

SQL

Support of SQL

SPARQL is used as query language

yes

APIs and other access methods

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

JDBC
ODBC
Thrift
Vega

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

All languages supporting JDBC/ODBC/Thrift
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

yes

proprietary syntax with the possibility to convert from PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Round robin

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

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

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

fine grained access rights according to SQL-standard

simple rights management via user accounts

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Video: Blazegraph Accelerates Graph Computing with GPUs
20 December 2015, insideHPC

Harnessing GPUs Delivers a Big Speedup for Graph Analytics
15 December 2015, Datanami

This AI Paper Introduces A Comprehensive RDF Dataset With Over 26 Billion Triples Covering Scholarly Data Across All Scientific Disciplines
19 August 2023, MarkTechPost

provided by Google News

Dr. Mike Flaxman, VP or Product Management at HEAVY.AI – Interview Series
19 September 2024, Unite.AI

5 Q’s for Mike Flaxman, Vice President of Heavy.AI
15 August 2024, Center for Data Innovation

HEAVY.AI Accelerates Big Data Analytics with Vultr's High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

HEAVY.AI Accelerates Big Data Analytics with Vultr’s High-Performance GPU Cloud Infrastructure
11 September 2024, insideBIGDATA

Meta delivers strong earnings, but weak guidance and heavy AI spending prompt investors to bail
24 April 2024, SiliconANGLE News

provided by Google News

Share this page