DBMS > Google BigQuery vs. RavenDB vs. Transbase vs. Valentina Server

System Properties Comparison Google BigQuery vs. RavenDB vs. Transbase vs. Valentina Server

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Open Source Operational and Transactional Enterprise NoSQL Document Database

A resource-optimized, high-performance, universally applicable RDBMS

Object-relational database and reports server

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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Trend Chart

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	2.84
Rank	#101	Overall
	#18	Document stores

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

cloud.google.com/bigquery

ravendb.net

www.transaction.de/en/products/transbase.html

www.valentina-db.net

Technical documentation

cloud.google.com/bigquery/docs

ravendb.net/docs

www.transaction.de/en/products/transbase/features.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Google

Hibernating Rhinos

Transaction Software GmbH

Paradigma Software

Initial release

2010

1987

1999

Current release

5.4, July 2022

Transbase 8.3, 2022

5.7.5

License

Commercial or Open Source

commercial

Open Source

AGPL version 3, commercial license available

commercial

free development license

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

C and C++

Server operating systems

hosted

Linux
macOS
Raspberry Pi
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like query language (RQL)

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
F#
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C
C#
C++
Java
JavaScript
Kotlin
Objective-C
PHP
Python

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID, Cluster-wide transaction available

yes

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

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

Google Cloud Identity & Access Management (IAM)

Authorization levels configured per client per database

fine grained access rights according to SQL-standard

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