DBMS > BigObject vs. Firebase Realtime Database vs. Microsoft SQL Server vs. Oracle Rdb vs. Riak KV

System Properties Comparison BigObject vs. Firebase Realtime Database vs. Microsoft SQL Server vs. Oracle Rdb vs. Riak KV

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Microsofts flagship relational DBMS

Distributed, fault tolerant key-value store

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Document store

Relational DBMS

Key-value store

with links between data sets and object tags for the creation of secondary indexes

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	0.13
Rank	#329	Overall
	#147	Relational DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	807.76
Rank	#3	Overall
	#3	Relational DBMS

Score	1.04
Rank	#184	Overall
	#84	Relational DBMS

Score	3.84
Rank	#76	Overall
	#9	Key-value stores

Website

bigobject.io

firebase.google.com/products/realtime-database

www.microsoft.com/en-us/sql-server

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

Technical documentation

docs.bigobject.io

firebase.google.com/docs/database

learn.microsoft.com/en-US/sql/sql-server

www.oracle.com/database/technologies/related/rdb-doc.html

www.tiot.jp/riak-docs/riak/kv/latest

Developer

BigObject, Inc.

Google

acquired by Google 2014

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

OpenSource, formerly Basho Technologies

Initial release

2015

2012

1989

1984

2009

Current release

SQL Server 2022, November 2022

7.4.1.1, 2021

3.2.0, December 2022

License

Commercial or Open Source

commercial

free community edition available

commercial

restricted free version is available

commercial

Open Source

Apache version 2, commercial enterprise edition

Cloud-based only

Only available as a cloud service

yes

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Implementation language

C++

Erlang

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
Windows

HP Open VMS

Linux
OS X

Data scheme

yes

schema-free

yes

Flexible Schema (defined schema, partial schema, schema free)

schema-free

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

Secondary indexes

yes

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

HTTP API
Native Erlang Interface

Supported programming languages

Java
JavaScript
Objective-C

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

Lua

limited functionality with using 'rules'

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

Erlang

Triggers

Callbacks are triggered when data changes

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

Sharding

no "single point of failure"

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, but depending on the SQL-Server Edition

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

none

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

yes

links between data sets can be stored

Transaction concepts

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

yes

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

yes, using Riak Security

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