DBMS > Google BigQuery vs. Kyligence Enterprise vs. MaxDB vs. RDF4J

System Properties Comparison Google BigQuery vs. Kyligence Enterprise vs. MaxDB vs. RDF4J

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed analytics engine for big data, built on top of Apache Kylin

A robust and reliable RDBMS optimized to run all major SAP solutions

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

Primary database model

Relational DBMS

RDF store

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.46
Rank	#266	Overall
	#124	Relational DBMS

Score	2.26
Rank	#114	Overall
	#55	Relational DBMS

Score	0.74
Rank	#222	Overall
	#9	RDF stores

Website

cloud.google.com/bigquery

kyligence.io/kyligence-enterprise

maxdb.sap.com

rdf4j.org

Technical documentation

cloud.google.com/bigquery/docs

maxdb.sap.com/documentation

rdf4j.org/documentation

Developer

Google

Kyligence, Inc.

SAP, acquired from Software AG (Adabas-D) in 1997

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

Initial release

2010

2016

1984

2004

Current release

7.9.10.12, February 2024

License

Commercial or Open Source

commercial

Limited community edition free

Open Source

Eclipse Distribution License (EDL), v1.0.

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

Java

C++

Java

Server operating systems

hosted

Linux

AIX
HP-UX
Linux
Solaris
Windows

Linux
OS X
Unix
Windows

Data scheme

yes

RDF Schemas

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.

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL for queries (using Apache Calcite)

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
WebDAV

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
Perl
PHP
Python

Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

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

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

In-memory capabilities

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

User concepts

Access control

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

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