DBMS > Apache Jena - TDB vs. Greenplum vs. Spark SQL vs. SWC-DB

System Properties Comparison Apache Jena - TDB vs. Greenplum vs. Spark SQL vs. SWC-DB

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Editorial information provided by DB-Engines

Name

Description

A RDF storage and query DBMS, shipped as an optional-use component of the Apache Jena framework

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Spark SQL is a component on top of 'Spark Core' for structured data processing

A high performance, scalable Wide Column DBMS

Primary database model

RDF store

Relational DBMS

Wide column store

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	3.62
Rank	#83	Overall
	#3	RDF stores

Score	8.08
Rank	#48	Overall
	#31	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

jena.apache.org/documentation/tdb/index.html

greenplum.org

spark.apache.org/sql

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

jena.apache.org/documentation/tdb/index.html

docs.greenplum.org

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Apache Software Foundation

originally developed by HP Labs

Pivotal Software Inc.

Apache Software Foundation

Alex Kashirin

Initial release

2000

2005

2014

2020

Current release

4.9.0, July 2023

7.0.0, September 2023

3.5.0 ( 2.13), September 2023

0.5, April 2021

License

Commercial or Open Source

Open Source

Apache License, Version 2.0

Open Source

Apache 2.0

Open Source

Apache 2.0

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Scala

C++

Server operating systems

All OS with a Java VM

Linux

Linux
OS X
Windows

Linux

Data scheme

yes

RDF Schemas

yes

schema-free

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.

yes

since Version 4.2

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

Fuseki

REST-style SPARQL HTTP Interface
Jena RDF API
RIO

RDF Input/Output

JDBC
ODBC

Proprietary protocol
Thrift

Supported programming languages

Java

C
Java
Perl
Python
R

Java
Python
R
Scala

C++

Server-side scripts

Stored procedures

yes

Triggers

yes

via event handler

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

ACID

TDB Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

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

User concepts

Access control

Access control via Jena Security

fine grained access rights according to SQL-standard

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