DBMS > Bangdb vs. DataFS vs. Google BigQuery vs. Oracle

System Properties Comparison Bangdb vs. DataFS vs. Google BigQuery vs. Oracle

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Editorial information provided by DB-Engines

Name

Description

Converged and high performance database for device data, events, time series, document and graph

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Large scale data warehouse service with append-only tables

Widely used RDBMS

Primary database model

Document store
Graph DBMS
Time Series DBMS

Object oriented DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Graph DBMS

Document store
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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

Score	0.16
Rank	#338	Overall
	#47	Document stores
	#32	Graph DBMS
	#31	Time Series DBMS

Score	0.09
Rank	#360	Overall
	#18	Object oriented DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	1244.08
Rank	#1	Overall
	#1	Relational DBMS

Website

bangdb.com

newdatabase.com

cloud.google.com/bigquery

www.oracle.com/database

Technical documentation

docs.bangdb.com

dev.mobiland.com/Overview.xsp

cloud.google.com/bigquery/docs

docs.oracle.com/en/database

Developer

Sachin Sinha, BangDB

Mobiland AG

Google

Oracle

Initial release

2012

2018

2010

1980

Current release

BangDB 2.0, October 2021

1.1.263, October 2022

23c, September 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

restricted free version is available

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, C++

C and C++

Server operating systems

Linux

Windows

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

Data scheme

schema-free

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

yes

Schemaless in JSON and XML columns

Typing

predefined data types such as float or date

yes: string, long, double, int, geospatial, stream, events

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

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

SQL like support with command line tool

yes

with proprietary extensions

APIs and other access methods

Proprietary protocol
RESTful HTTP API

.NET Client API
Proprietary client DLL
WinRT client

RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

C
C#
C++
Java
Python

.Net
C
C#
C++
VB.Net

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

Server-side scripts

Stored procedures

user defined functions

in JavaScript

PL/SQL

also stored procedures in Java possible

Triggers

yes, Notifications (with Streaming only)

no, except callback-events from server when changes happened

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Proprietary Sharding system

none

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor, Knob for CAP (enterprise version only)

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

can be realized in PL/SQL

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable consistency, set CAP knob accordingly

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

ACID

isolation level can be parameterized

Concurrency

Support for concurrent manipulation of data

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes, run db with in-memory only mode

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

yes (enterprise version only)

Windows-Profile

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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