DBMS > Atos Standard Common Repository vs. Blazegraph vs. Spark SQL vs. Yaacomo

System Properties Comparison Atos Standard Common Repository vs. Blazegraph vs. Spark SQL vs. Yaacomo

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Editorial information provided by DB-Engines

Name

This system has been discontinued and will be removed from the DB-Engines ranking.

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

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Highly scalable database system, designed for managing session and subscriber data in modern mobile communication networks

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

Spark SQL is a component on top of 'Spark Core' for structured data processing

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Key-value store

Graph DBMS
RDF store

Relational DBMS

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

Score	0.81
Rank	#213	Overall
	#19	Graph DBMS
	#8	RDF stores

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

atos.net/en/convergence-creators/portfolio/standard-common-repository

blazegraph.com

spark.apache.org/sql

yaacomo.com

Technical documentation

wiki.blazegraph.com

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Atos Convergence Creators

Blazegraph

Apache Software Foundation

Q2WEB GmbH

Initial release

2016

2006

2014

2009

Current release

1703

2.1.5, March 2019

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

extended commercial license available

Open Source

Apache 2.0

commercial

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

Scala

Server operating systems

Linux

Linux
OS X
Windows

Android
Linux
Windows

Data scheme

Schema and schema-less with LDAP views

schema-free

yes

Typing

predefined data types such as float or date

optional

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.

yes

Secondary indexes

yes

SQL

Support of SQL

SPARQL is used as query language

SQL-like DML and DDL statements

yes

APIs and other access methods

LDAP

Java API
RESTful HTTP API
SPARQL QUERY
SPARQL UPDATE
TinkerPop 3

JDBC
ODBC

Supported programming languages

All languages with LDAP bindings

.Net
C
C++
Java
JavaScript
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

cell division

Sharding

yes, utilizing Spark Core

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication

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

Atomic execution of specific operations

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

LDAP bind authentication

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

fine grained access rights according to SQL-standard

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