DBMS > Firebolt vs. Google BigQuery vs. Quasardb vs. searchxml

System Properties Comparison Firebolt vs. Google BigQuery vs. Quasardb vs. searchxml

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Editorial information provided by DB-Engines

Name

Description

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Large scale data warehouse service with append-only tables

Distributed, high-performance timeseries database

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Relational DBMS

Time Series DBMS

Native XML DBMS
Search engine

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

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

www.firebolt.io

cloud.google.com/bigquery

quasar.ai

www.searchxml.net/category/products

Technical documentation

docs.firebolt.io

cloud.google.com/bigquery/docs

doc.quasar.ai/master

www.searchxml.net/support/handouts

Developer

Firebolt Analytics Inc.

Google

quasardb

informationpartners gmbh

Initial release

2020

2010

2009

2015

Current release

3.14.1, January 2024

1.0

License

Commercial or Open Source

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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++

Server operating systems

hosted

BSD
Linux
OS X
Windows

Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

integer and binary

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

Secondary indexes

yes

with tags

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

.Net
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

HTTP API

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

Go
JavaScript (Node.js)
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

on the application server

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

Source-replica replication with selectable replication factor

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

Transient mode

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Cryptographically strong user authentication and audit trail

Domain, group and role-based access control at the document level and for application services

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