DBMS > gStore vs. Microsoft Azure Synapse Analytics vs. Postgres-XL vs. RDFox vs. Vertica

System Properties Comparison gStore vs. Microsoft Azure Synapse Analytics vs. Postgres-XL vs. RDFox vs. Vertica

Editorial information provided by DB-Engines

Name

Description

A native Graph DBMS to store and maintain very large RDF datasets.

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Based on PostgreSQL enhanced with MPP and write-scale-out cluster features

High performance knowledge graph and semantic reasoning engine

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

Column oriented

Secondary database models

Document store
Spatial DBMS

Relational DBMS

Spatial DBMS
Time Series DBMS

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

Score	0.03
Rank	#367	Overall
	#37	Graph DBMS
	#18	RDF stores

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	0.43
Rank	#260	Overall
	#119	Relational DBMS

Score	0.22
Rank	#305	Overall
	#25	Graph DBMS
	#15	RDF stores

Score	9.62
Rank	#42	Overall
	#26	Relational DBMS

Website

en.gstore.cn

azure.microsoft.com/services/synapse-analytics

www.postgres-xl.org

www.oxfordsemantic.tech

www.vertica.com

Technical documentation

en.gstore.cn/#/enDocs

docs.microsoft.com/azure/synapse-analytics

www.postgres-xl.org/documentation

docs.oxfordsemantic.tech

vertica.com/documentation

Developer

Microsoft

Oxford Semantic Technologies

OpenText

previously Micro Focus and Hewlett Packard

Initial release

2016

2014

since 2012, originally named StormDB

2017

2005

Current release

1.2, November 2023

10 R1, October 2018

6.0, Septermber 2022

12.0.3, January 2023

License

Commercial or Open Source

Open Source

BSD

commercial

Open Source

Mozilla public license

commercial

Limited community edition free

Cloud-based only

Only available as a cloud service

yes

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++

Server operating systems

Linux

hosted

Linux
macOS

Linux
macOS
Windows

Linux

Data scheme

schema-free and OWL/RDFS-schema support

yes

RDF schemas

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

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

XML type, but no XML query functionality

Secondary indexes

yes

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

SQL

Support of SQL

yes

distributed, parallel query execution

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

APIs and other access methods

HTTP API
SPARQL 1.1

ADO.NET
JDBC
ODBC

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API
SPARQL 1.1

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

character-based, interactive, front-end utility

Supported programming languages

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

C#
Java
PHP

.Net
C
C++
Delphi
Erlang
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

C
Java

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

Server-side scripts

Stored procedures

yes

Transact SQL

user defined functions

yes, PostgreSQL PL/pgSQL, with minor differences

Triggers

yes

yes, called Custom Alerts

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

horizontal partitioning

horizontal partitioning, hierarchical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

replication via a shared file system

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

Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

Immediate Consistency

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

Transaction concepts

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

yes

ACID

MVCC

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.

yes

User concepts

Access control

Users, roles and permissions, Role-Based Access Control (RBAC) supported

yes

fine grained access rights according to SQL-standard

Roles, resources, and access types

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

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