DBMS > IBM Db2 Event Store vs. Oracle NoSQL vs. RDFox vs. Snowflake

System Properties Comparison IBM Db2 Event Store vs. Oracle NoSQL vs. RDFox vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

Distributed Event Store optimized for Internet of Things use cases

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

High performance knowledge graph and semantic reasoning engine

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

Primary database model

Event Store
Time Series DBMS

Document store
Key-value store
Relational DBMS

Graph DBMS
RDF store

Relational DBMS

Secondary database models

Relational 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	3.05
Rank	#97	Overall
	#17	Document stores
	#16	Key-value stores
	#50	Relational DBMS

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

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

www.oracle.com/database/nosql/technologies/nosql

www.oxfordsemantic.tech

www.snowflake.com

Technical documentation

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

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.oxfordsemantic.tech

docs.snowflake.net/manuals/index.html

Developer

IBM

Oracle

Oxford Semantic Technologies

Snowflake Computing Inc.

Initial release

2017

2011

2017

2014

Current release

2.0

23.3, December 2023

6.0, Septermber 2022

License

Commercial or Open Source

commercial

free developer edition available

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

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

C++

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

Linux
Solaris SPARC/x86

Linux
macOS
Windows

hosted

Data scheme

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

RDF schemas

yes

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

Typing

predefined data types such as float or date

yes

optional

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

through the embedded Spark runtime

SQL-like DML and DDL statements

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

RESTful HTTP API
SPARQL 1.1

CLI Client
JDBC
ODBC

Supported programming languages

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

C
C#
Go
Java
JavaScript (Node.js)
Python

C
Java

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

replication via a shared file system

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

configurable

ACID within a storage node (=shard)

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 capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

off heap cache

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users and roles

Roles, resources, and access types

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

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