DBMS > EDB Postgres vs. Google BigQuery vs. ObjectBox vs. TimesTen vs. Yanza

System Properties Comparison EDB Postgres vs. Google BigQuery vs. ObjectBox vs. TimesTen vs. Yanza

Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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

Extremely fast embedded database for small devices, IoT and Mobile

In-Memory RDBMS compatible to Oracle

Time Series DBMS for IoT Applications

Primary database model

Secondary database models

Document store
Spatial DBMS

Time Series DBMS

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

Score	1.91
Rank	#130	Overall
	#60	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

www.enterprisedb.com

cloud.google.com/bigquery

objectbox.io

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

yanza.com

Technical documentation

www.enterprisedb.com/docs

cloud.google.com/bigquery/docs

docs.objectbox.io

docs.oracle.com/database/timesten-18.1

Developer

EnterpriseDB

Google

ObjectBox Limited

Oracle, TimesTen Performance Software, HP

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

Yanza

Initial release

2005

2010

2017

1998

2015

Current release

14, December 2021

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

BSD for PostgreSQL-components

commercial

Open Source

Apache License 2.0

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C and C++

Server operating systems

Linux
Windows

hosted

Android
iOS
Linux
macOS
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Windows

Data scheme

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

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

Secondary indexes

yes

SQL

Support of SQL

yes

standard with numerous extensions

yes

APIs and other access methods

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

RESTful HTTP/JSON API

Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

HTTP API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C
C++
Java
PL/SQL

any language that supports HTTP calls

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

PL/SQL

Triggers

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

by hash, list or range

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

online/offline synchronization between client and server

Multi-source replication
Source-replica replication

none

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

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

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

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)

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

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