DBMS > Firebolt vs. Google BigQuery vs. SQream DB vs. TimesTen

System Properties Comparison Firebolt vs. Google BigQuery vs. SQream DB vs. TimesTen

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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

a GPU-based, columnar RDBMS for big data analytics workloads

An in-memory SQL relational database that delivers microsecond response and high throughput for OLTP applications. TimesTen can be deployed as a standalone database or as a cache to a backend Oracle database.

Primary database model

Relational DBMS

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

Score	1.49
Rank	#148	Overall
	#68	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Score	0.62
Rank	#234	Overall
	#109	Relational DBMS

Score	1.26
Rank	#164	Overall
	#75	Relational DBMS

Website

www.firebolt.io

cloud.google.com/bigquery

sqream.com

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.firebolt.io

cloud.google.com/bigquery/docs

docs.sqream.com

docs.oracle.com/en/database/other-databases/timesten/index.html

Developer

Firebolt Analytics Inc.

Google

SQream Technologies

Oracle

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2020

2010

2017

1998

Current release

2022.1.6, December 2022

Release 22.1

License

Commercial or Open Source

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++, CUDA, Haskell, Java, Scala

Server operating systems

hosted

Linux

IBM AIX Power PC 64-bit
Linux arm64
Linux x86-64
Solaris SPARC 64
Solaris SPARC/x86
Solaris x86-64

Data scheme

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

APIs and other access methods

.Net
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

.Net
JDBC
ODBC

ODBC
ODP.NET
Oracle Call Interface (OCI)
Pro*C/C++ programming interfaces
SQL and PL/SQL via JDBC

Supported programming languages

Go
JavaScript (Node.js)
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C
C++
Java
Node.js
PL/SQL
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions in Python

PL/SQL

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

horizontal and vertical partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

depending on storage layer

none

Multi-source replication
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

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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)

fine grained access rights according to SQL-standard

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