DBMS > EXASOL vs. RDF4J vs. SiteWhere vs. SwayDB vs. Weaviate

System Properties Comparison EXASOL vs. RDF4J vs. SiteWhere vs. SwayDB vs. Weaviate

Editorial information provided by DB-Engines

Name

Description

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

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

M2M integration platform for persisting/querying time series data

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

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

Score	2.25
Rank	#120	Overall
	#57	Relational DBMS

Score	0.71
Rank	#231	Overall
	#9	RDF stores

Score	0.06
Rank	#367	Overall
	#36	Time Series DBMS

Score	0.04
Rank	#373	Overall
	#57	Key-value stores

Score	1.89
Rank	#138	Overall
	#4	Vector DBMS

Website

www.exasol.com

rdf4j.org

github.com/sitewhere/sitewhere

swaydb.simer.au

github.com/weaviate/weaviate
weaviate.io

Technical documentation

www.exasol.com/resources

rdf4j.org/documentation

sitewhere1.sitewhere.io/index.html

weaviate.io/developers/weaviate

Developer

Exasol

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

SiteWhere

Simer Plaha

Weaviate B.V.

Initial release

2000

2004

2010

2018

2019

Current release

1.19, May 2023

License

Commercial or Open Source

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

Common Public Attribution License Version 1.0

Open Source

GNU Affero GPL V3.0

Open Source

commercial license available with Weaviate Enterprise

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
OS X
Unix
Windows

Linux
OS X
Windows

Data scheme

yes

RDF Schemas

predefined scheme

schema-free

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

yes

GraphQL is used as query language

APIs and other access methods

.Net
JDBC
ODBC
WebSocket

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

HTTP REST

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

Java
Lua
Python
R

Java
PHP
Python

Java
Kotlin
Scala

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on HBase

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on HBase

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

Atomic execution of operations

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

Access rights for users, groups and roles according to SQL-standard

Users with fine-grained authorization concept

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
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Competitive advantages

Flexible deployment - Free, open source or fully-managed cloud vector database service...
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Typical application scenarios

As a database supporting the development of generative AI and semantic search applications...
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Key customers

All companies that have data.
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Market metrics

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
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Licensing and pricing models

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

DB-Engines blog posts

Weaviate, an ANN Database with CRUD support
2 February 2021, Etienne Dilocker, SeMI Technologies (sponsor)

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