DBMS > Badger vs. Netezza vs. RDF4J vs. Snowflake vs. VelocityDB

System Properties Comparison Badger vs. Netezza vs. RDF4J vs. Snowflake vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

An embeddable, persistent, simple and fast Key-Value Store, written purely in Go.

Data warehouse and analytics appliance part of IBM PureSystems

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Cloud-based data warehousing service for structured and semi-structured data

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Graph DBMS
Object oriented DBMS

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

Score	0.22
Rank	#320	Overall
	#47	Key-value stores

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

github.com/dgraph-io/badger

www.ibm.com/products/netezza

rdf4j.org

www.snowflake.com

velocitydb.com

Technical documentation

godoc.org/github.com/dgraph-io/badger

rdf4j.org/documentation

docs.snowflake.net/manuals/index.html

velocitydb.com/UserGuide

Developer

DGraph Labs

IBM

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Snowflake Computing Inc.

VelocityDB Inc

Initial release

2017

2000

2004

2014

2011

Current release

7.x

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

BSD
Linux
OS X
Solaris
Windows

Linux

included in appliance

Linux
OS X
Unix
Windows

hosted

Any that supports .NET

Data scheme

schema-free

yes

RDF Schemas

yes

support of semi-structured data formats (JSON, XML, Avro)

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

JDBC
ODBC
OLE DB

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

CLI Client
JDBC
ODBC

.Net

Supported programming languages

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
PHP
Python

JavaScript (Node.js)
Python

.Net

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

Users with fine-grained authorization concept

Users with fine-grained authorization concept, user roles and pluggable authentication

Based on Windows Authentication

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