DBMS > Dolt vs. Google BigQuery vs. Kinetica vs. Transbase vs. VelocityDB

System Properties Comparison Dolt vs. Google BigQuery vs. Kinetica vs. Transbase vs. VelocityDB

Editorial information provided by DB-Engines

Name

Description

A MySQL compatible DBMS with Git-like versioning of data and schema

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

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

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Relational DBMS

Graph DBMS
Object oriented DBMS

Secondary database models

Document store

Spatial DBMS
Time Series DBMS

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

Score	1.02
Rank	#191	Overall
	#89	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.17
Rank	#334	Overall
	#148	Relational DBMS

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

github.com/dolthub/dolt
www.dolthub.com

cloud.google.com/bigquery

www.kinetica.com

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

velocitydb.com

Technical documentation

docs.dolthub.com

cloud.google.com/bigquery/docs

docs.kinetica.com

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

velocitydb.com/UserGuide

Developer

DoltHub Inc

Google

Kinetica

Transaction Software GmbH

VelocityDB Inc

Initial release

2018

2010

2012

1987

2011

Current release

7.1, August 2021

Transbase 8.3, 2022

7.x

License

Commercial or Open Source

Open Source

Apache Version 2.0

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, C++

C and C++

Server operating systems

Linux
macOS
Windows

hosted

Linux

FreeBSD
Linux
macOS
Solaris
Windows

Any that supports .NET

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.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

CLI Client
HTTP REST

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

.Net

Supported programming languages

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

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

.Net

Server-side scripts

Stored procedures

yes

currently in alpha release

user defined functions

in JavaScript

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

yes

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

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

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

yes

ACID

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

GPU vRAM or System RAM

yes

User concepts

Access control

Only one user is configurable, and must be specified in the config file at startup

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Based on Windows Authentication

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