DBMS > EsgynDB vs. Google BigQuery vs. IBM Db2 vs. RDF4J vs. Transwarp Hippo

System Properties Comparison EsgynDB vs. Google BigQuery vs. IBM Db2 vs. RDF4J vs. Transwarp Hippo

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Large scale data warehouse service with append-only tables

Common in IBM host environments, 2 different versions for host and Windows/Linux

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

Cloud-native distributed Vector DBMS that supports storage, retrieval, and management of massive vector-based datasets

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

RDF store

Vector DBMS

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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

Score	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	123.05
Rank	#9	Overall
	#6	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Score	0.00
Rank	#385	Overall
	#16	Vector DBMS

Website

www.esgyn.cn

cloud.google.com/bigquery

www.ibm.com/products/db2

rdf4j.org

www.transwarp.cn/en/subproduct/hippo

Technical documentation

cloud.google.com/bigquery/docs

www.ibm.com/docs/en/db2

rdf4j.org/documentation

Developer

Esgyn

Google

IBM

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

Initial release

2015

2010

1983

host version

2004

2023

Current release

12.1, October 2016

1.0, May 2023

License

Commercial or Open Source

commercial

free version is available

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

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

Implementation language

C++, Java

C and C++

Java

C++

Server operating systems

Linux

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

Linux
OS X
Unix
Windows

Linux
macOS

Data scheme

yes

RDF Schemas

Typing

predefined data types such as float or date

yes

Vector, Numeric and String

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

SQL

Support of SQL

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

RESTful HTTP/JSON API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

Java
PHP
Python

C++
Java
Python

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

only with Windows/Unix/Linux Version

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

with separate tools (MQ, InfoSphere)

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Isolation support depends on the API used

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

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

fine grained access rights according to SQL-standard

Role based access control and fine grained access rights

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