DBMS > EDB Postgres vs. Google BigQuery vs. TerminusDB vs. TimesTen

System Properties Comparison EDB Postgres vs. Google BigQuery vs. TerminusDB vs. TimesTen

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Editorial information provided by DB-Engines

Name

Description

The EDB Postgres Platform is an enterprise-class data management platform based on the open source database PostgreSQL with flexible deployment options and Oracle compatibility features, complemented by tool kits for management, integration, and migration.

Large scale data warehouse service with append-only tables

Scalable Graph Database platform making enterprise data available by exploiting inferred entities and relationships

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

Secondary database models

Document store
Spatial DBMS

Document store
RDF store

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

Score	1.86
Rank	#131	Overall
	#61	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.19
Rank	#311	Overall
	#27	Graph DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.enterprisedb.com

cloud.google.com/bigquery

terminusdb.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

www.enterprisedb.com/docs

cloud.google.com/bigquery/docs

terminusdb.github.io/terminusdb/#

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

EnterpriseDB

Google

DataChemist Ltd.

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2005

2010

2018

1998

Current release

14, December 2021

11.0.0, January 2023

Release 22.1

License

Commercial or Open Source

commercial

BSD for PostgreSQL-components

commercial

Open Source

GPL V3

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

Prolog, Rust

Server operating systems

Linux
Windows

hosted

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

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

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

yes

standard with numerous extensions

yes

SQL-like query language (WOQL)

yes

APIs and other access methods

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

RESTful HTTP/JSON API

OWL
RESTful HTTP API
WOQL (Web Object Query Language)

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
PHP
Python
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

in JavaScript

yes

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

by hash, list or range

none

Graph Partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Journaling Streams

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

in-memory journaling

yes

by means of logfiles and checkpoints

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)

Role-based access control

fine grained access rights according to SQL-standard

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