DBMS > BoltDB vs. Google BigQuery vs. Kinetica vs. Linter vs. Transbase

System Properties Comparison BoltDB vs. Google BigQuery vs. Kinetica vs. Linter vs. Transbase

Editorial information provided by DB-Engines

Name

Description

An embedded key-value store for Go.

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

RDBMS for high security requirements

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

Primary database model

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.70
Rank	#225	Overall
	#31	Key-value stores

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.42
Rank	#261	Overall
	#120	Relational DBMS

Score	0.03
Rank	#368	Overall
	#156	Relational DBMS

Score	0.05
Rank	#355	Overall
	#150	Relational DBMS

Website

github.com/boltdb/bolt

cloud.google.com/bigquery

www.kinetica.com

linter.ru

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

Technical documentation

cloud.google.com/bigquery/docs

docs.kinetica.com

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

Developer

Google

Kinetica

relex.ru

Transaction Software GmbH

Initial release

2013

2010

2012

1990

1987

Current release

7.1, August 2021

Transbase 8.3, 2022

License

Commercial or Open Source

Open Source

MIT License

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

C, C++

C and C++

Server operating systems

BSD
Linux
OS X
Solaris
Windows

hosted

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

FreeBSD
Linux
macOS
Solaris
Windows

Data scheme

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 DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

ADO.NET
JDBC
ODBC
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

yes

proprietary syntax with the possibility to convert from PL/SQL

yes

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

none

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

yes

Since BigQuery is designed for querying data

ACID

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

GPU vRAM or System RAM

User concepts

Access control

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

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