DBMS > Fujitsu Enterprise Postgres vs. GBase vs. GeoSpock vs. Google BigQuery

System Properties Comparison Fujitsu Enterprise Postgres vs. GBase vs. GeoSpock vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

GeoSpock seems to be discontinued. Therefore it will be excluded from the DB-Engines ranking.

Description

Enterprise-grade PostgreSQL-based DBMS with security enhancements such as Transparent Data Encryption and Data Masking, plus high-availability and performance improvement features.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Spatial and temporal data processing engine for extreme data scale

Large scale data warehouse service with append-only tables

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	0.22
Rank	#304	Overall
	#137	Relational DBMS

Score	1.11
Rank	#173	Overall
	#78	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Website

www.postgresql.fastware.com

www.gbase.cn

geospock.com

cloud.google.com/bigquery

Technical documentation

www.postgresql.fastware.com/product-manuals

cloud.google.com/bigquery/docs

Developer

PostgreSQL Global Development Group, Fujitsu Australia Software Technology

General Data Technology Co., Ltd.

GeoSpock

Google

Initial release

2004

2010

Current release

Fujitsu Enterprise Postgres 14, January 2022

GBase 8a, GBase 8s, GBase 8c

2.0, September 2019

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, Java, Python

Java, Javascript

Server operating systems

Linux
Windows

Linux

hosted

Data scheme

yes

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

Secondary indexes

yes

temporal, categorical

SQL

Support of SQL

yes

Standard with numerous extensions

ANSI SQL for query only (using Presto)

yes

APIs and other access methods

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

ADO.NET
C API
JDBC
ODBC

JDBC

RESTful HTTP/JSON API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

partitioning by range, list and by hash

horizontal partitioning (by range, list and hash) and vertical partitioning

Automatic sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

yes

Access rights for users can be defined per table

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

Google Cloud Identity & Access Management (IAM)

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