DBMS > Bangdb vs. GraphDB vs. SiriDB vs. Stardog vs. Vertica

System Properties Comparison Bangdb vs. GraphDB vs. SiriDB vs. Stardog vs. Vertica

Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

Enterprise-ready RDF and graph database with efficient reasoning, cluster and external index synchronization support. It supports also SQL JDBC access to Knowledge Graph and GraphQL over SPARQL.

Open Source Time Series DBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Cloud or off-cloud analytical database and query engine for structured and semi-structured streaming and batch data. Machine learning platform with built-in algorithms, data preparation capabilities, and model evaluation and management via SQL or Python.

Primary database model

Document store
Graph DBMS
Time Series DBMS

Column oriented

Secondary database models

Spatial DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	3.32
Rank	#91	Overall
	#6	Graph DBMS
	#4	RDF stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Score	10.68
Rank	#43	Overall
	#27	Relational DBMS

Website

Technical documentation

docs.bangdb.com

graphdb.ontotext.com/documentation

docs.siridb.com

docs.stardog.com

vertica.com/documentation

Social network pages

Developer

Sachin Sinha, BangDB

Ontotext

Cesbit

Stardog-Union

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2012

2000

2017

2010

2005

Current release

BangDB 2.0, October 2021

10.4, October 2023

7.3.0, May 2020

12.0.3, January 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

Some plugins of GraphDB Workbench are open sourced

Open Source

MIT License

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

on-premises, all major clouds - Amazon AWS, Microsoft Azure, Google Cloud Platform and containers

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

Java

C++

Server operating systems

Linux

All OS with a Java VM
Linux
OS X
Windows

Linux

Linux
macOS
Windows

Linux

Data scheme

schema-free

schema-free and OWL/RDFS-schema support; RDF shapes

yes

schema-free and OWL/RDFS-schema support

Yes, but also semi-structure/unstructured data storage, and complex hierarchical data (like Parquet) stored and/or queried.

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

yes

Numeric data

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.

Import/export of XML data possible

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes, supports real-time synchronization and indexing in SOLR/Elastic search/Lucene and GeoSPARQL geometry data indexes

yes

supports real-time indexing in full-text and geospatial

No Indexes Required. Different internal optimization strategy, but same functionality included.

SQL

Support of SQL

SQL like support with command line tool

stored SPARQL accessed as SQL using Apache Calcite through JDBC/ODBC

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

Full 1999 standard plus machine learning, time series and geospatial. Over 650 functions.

APIs and other access methods

Proprietary protocol
RESTful HTTP API

GeoSPARQL
GraphQL
GraphQL Federation
Java API
JDBC
RDF4J API
RDFS
RIO
Sail API
Sesame REST HTTP Protocol
SPARQL 1.1

HTTP API

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

ADO.NET
JDBC
Kafka Connector
ODBC
RESTful HTTP API
Spark Connector
vSQL

character-based, interactive, front-end utility

Supported programming languages

C
C#
C++
Java
Python

.Net
C#
Clojure
Java
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R

Server-side scripts

Stored procedures

well-defined plugin interfaces; JavaScript server-side extensibility

user defined functions and aggregates, HTTP Server extensions in Java

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes, Notifications (with Streaming only)

yes

via event handlers

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

none

Sharding

none

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication

yes

Multi-source replication in HA-Cluster

Multi-source replication

One, or more copies of data replicated across nodes, or object-store used for repository.

MapReduce

Offers an API for user-defined Map/Reduce methods

Bi-directional Spark integration

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

Immediate Consistency, Eventual consistency (configurable in cluster mode per master or individual client request)

Immediate Consistency in HA-Cluster

Immediate Consistency

Foreign keys

Referential integrity

yes

Constraint checking

yes

relationships in graphs

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

User concepts

Access control

yes (enterprise version only)

Default Basic authentication through RDF4J client, or via Java when run with cURL, default token-based in the Workbench or via Rest API, optional access through OpenID or Kerberos single sign-on.

simple rights management via user accounts

Access rights for users and roles

fine grained access rights according to SQL-standard; supports Kerberos, LDAP, Ident and hash

More information provided by the system vendor

Specific characteristics

Ontotext GraphDB is a semantic database engine that allows organizations to build...
» more

Deploy-anywhere database for large-scale analytical deployments. Deploy off-cloud,...
» more

Competitive advantages

GraphDB allows you to link text and data in big knowledge graphs. It’s easy to experiment...
» more

Fast, scalable, and capable of high concurrency. Separation of compute/storage leverages...
» more

Typical application scenarios

Metadata enrichment and management, linked data publishing, semantic inferencing...
» more

Communication and network analytics, Embedded analytics, Fraud monitoring and Risk...
» more

Key customers

GraphDB provides a platform for building next-generation AI and Knowledge Graph...
» more

Abiba Systems, Adform, adMarketplace, AmeriPride, Anritsu, AOL, Avito, Auckland Transport,...
» more

Market metrics

GraphDB is the most utilized semantic triplestore for mission-critical enterprise...
» more

Licensing and pricing models

GraphDB Free is a non-commercial version and is free to use. GraphDB Enterprise edition...
» more

Cost-based models and subscription-based models are both available. One license is...
» more

News

Understanding the Graph Center of Excellence
17 May 2024

Migrating From LPG to RDF Graph Model
8 May 2024

Case study: Policy Enforcement Automation With Semantics
2 May 2024

Okay, RAG… We Have a Problem
26 April 2024

Scaling Understanding with the Help of Feedback Loops, Knowledge Graphs and NLP
19 April 2024

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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