DBMS > Google BigQuery vs. NSDb vs. OrigoDB vs. Transbase

System Properties Comparison Google BigQuery vs. NSDb vs. OrigoDB vs. Transbase

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A fully ACID in-memory object graph database

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

Primary database model

Relational DBMS

Time Series DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#396	Overall
	#42	Time Series DBMS

Score	0.03
Rank	#378	Overall
	#51	Document stores
	#18	Object oriented DBMS

Score	0.15
Rank	#337	Overall
	#147	Relational DBMS

Website

cloud.google.com/bigquery

nsdb.io

origodb.com

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

Technical documentation

cloud.google.com/bigquery/docs

nsdb.io/Architecture

origodb.com/docs

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

Developer

Google

Robert Friberg et al

Transaction Software GmbH

Initial release

2010

2017

2009

under the name LiveDB

1987

Current release

Transbase 8.3, 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

commercial

free development license

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

Java, Scala

C and C++

Server operating systems

hosted

Linux
macOS

Linux
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

gRPC
HTTP REST
WebSocket

.NET Client API
HTTP API
LINQ

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Scala

.Net

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Domain Events

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

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

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

depending on model

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

Write ahead log

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)

Role based authorization

fine grained access rights according to SQL-standard

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