DBMS > Faircom DB vs. Google BigQuery vs. IBM Db2 warehouse vs. RethinkDB vs. SQream DB

System Properties Comparison Faircom DB vs. Google BigQuery vs. IBM Db2 warehouse vs. RethinkDB vs. SQream DB

Editorial information provided by DB-Engines

Name

Description

Native high-speed multi-model DBMS for relational and key-value store data simultaneously accessible through SQL and NoSQL APIs.

Large scale data warehouse service with append-only tables

Cloud-based data warehousing service

DBMS for the Web with a mechanism to push updated query results to applications in realtime.

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

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#304	Overall
	#43	Key-value stores
	#136	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1.37
Rank	#160	Overall
	#74	Relational DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

www.faircom.com/products/faircom-db

cloud.google.com/bigquery

www.ibm.com/products/db2/warehouse

rethinkdb.com

sqream.com

Technical documentation

docs.faircom.com/docs/en/UUID-7446ae34-a1a7-c843-c894-d5322e395184.html

cloud.google.com/bigquery/docs

rethinkdb.com/docs

docs.sqream.com

Developer

FairCom Corporation

Google

IBM

The Linux Foundation

since July 2017

SQream Technologies

Initial release

1979

2010

2014

2009

2017

Current release

V12, November 2020

2.4.1, August 2020

2022.1.6, December 2022

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

Apache Version 2

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

ANSI C, C++

C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

AIX
FreeBSD
HP-UX
Linux
NetBSD
OS X
QNX
SCO
Solaris
VxWorks
Windows

easily portable to other OSs

hosted

Linux
OS X
Windows

Linux

Data scheme

schema free, schema optional, schema required, partial schema,

yes

schema-free

yes

Typing

predefined data types such as float or date

yes, ANSI SQL Types, JSON, typed binary structures

yes

string, binary, float, bool, date, geometry

yes, ANSI Standard SQL Types

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.

Import/export of XML data possible

Secondary indexes

yes

SQL

Support of SQL

yes, ANSI SQL with proprietary extensions

yes

APIs and other access methods

ADO.NET
Direct SQL
JDBC
JPA
ODBC
RESTful HTTP/JSON API
RESTful MQTT/JSON API
RPC

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
OLE DB

.Net
JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
Java
JavaScript (Node.js and browser)
PHP
Python
Visual Basic

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

PL/SQL, SQL PL

user defined functions in Python

Triggers

yes

Client-side triggers through changefeeds

Partitioning methods

Methods for storing different data on different nodes

File partitioning, horizontal partitioning, sharding

Customizable business rules for table partitioning

none

Sharding

range based

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes, configurable to be parallel or serial, synchronous or asynchronous, uni-directional or bi-directional, ACID-consistent or eventually consistent (with custom conflict resolution).

yes

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency
Tunable consistency per server, database, table, and transaction

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

tunable from ACID to Eventually Consistent

Since BigQuery is designed for querying data

ACID

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

yes

Durability

Support for making data persistent

Yes, tunable from durable to delayed durability to in-memory

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 with additional protections for files

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

yes

users and table-level permissions

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