DBMS > IBM Db2 Event Store vs. RDF4J vs. Riak KV vs. Snowflake vs. VelocityDB

System Properties Comparison IBM Db2 Event Store vs. RDF4J vs. Riak KV vs. Snowflake vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

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

Distributed, fault tolerant key-value store

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

Event Store
Time Series DBMS

RDF store

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Relational DBMS

Graph DBMS
Object oriented DBMS

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

Score	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Score	4.01
Rank	#79	Overall
	#9	Key-value stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

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

Website

www.ibm.com/products/db2-event-store

rdf4j.org

www.snowflake.com

velocitydb.com

Technical documentation

www.ibm.com/docs/en/db2-event-store

rdf4j.org/documentation

www.tiot.jp/riak-docs/riak/kv/latest

docs.snowflake.net/manuals/index.html

velocitydb.com/UserGuide

Developer

IBM

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

OpenSource, formerly Basho Technologies

Snowflake Computing Inc.

VelocityDB Inc

Initial release

2017

2004

2009

2014

2011

Current release

2.0

3.2.0, December 2022

7.x

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Apache version 2, commercial enterprise edition

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C and C++

Java

Erlang

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
OS X
Unix
Windows

Linux
OS X

hosted

Any that supports .NET

Data scheme

yes

RDF Schemas

schema-free

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

restricted

yes

SQL

Support of SQL

yes

through the embedded Spark runtime

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

.Net

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

Java
PHP
Python

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

.Net

Server-side scripts

Stored procedures

yes

Erlang

user defined functions

Triggers

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

no "single point of failure"

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

links between data sets can be stored

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

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

yes, using Riak Security

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

Based on Windows Authentication

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