DBMS > atoti vs. Brytlyt vs. Firebolt vs. Google BigQuery

System Properties Comparison atoti vs. Brytlyt vs. Firebolt vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Highly scalable cloud data warehouse and analytics product

Forked from Clickhouse

Large scale data warehouse service with append-only tables

Primary database model

Object oriented DBMS

Relational DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	0.38
Rank	#276	Overall
	#127	Relational DBMS

Score	1.73
Rank	#140	Overall
	#63	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

atoti.io

brytlyt.io

www.firebolt.io

cloud.google.com/bigquery

Technical documentation

docs.atoti.io

docs.brytlyt.io

docs.firebolt.io

cloud.google.com/bigquery/docs

Developer

ActiveViam

Brytlyt

Firebolt Analytics Inc.

Google

Initial release

2016

2020

2010

Current release

5.0, August 2023

License

Commercial or Open Source

commercial

free versions available

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

Java

C, C++ and CUDA

Server operating systems

Linux
OS X
Windows

hosted

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.

yes

specific XML-type available, but no XML query functionality.

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

.Net
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

Go
JavaScript (Node.js)
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

Python

user defined functions

in PL/pgSQL

user defined functions

in JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

depending on storage layer

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

fine grained access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

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