DBMS > Bangdb vs. Google BigQuery vs. Microsoft Azure SQL Database vs. Oracle vs. Yaacomo

System Properties Comparison Bangdb vs. Google BigQuery vs. Microsoft Azure SQL Database vs. Oracle vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

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

Large scale data warehouse service with append-only tables

Database as a Service offering with high compatibility to Microsoft SQL Server

Widely used RDBMS

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial 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	0.08
Rank	#347	Overall
	#47	Document stores
	#34	Graph DBMS
	#31	Time Series DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	1236.29
Rank	#1	Overall
	#1	Relational DBMS

Website

bangdb.com

cloud.google.com/bigquery

azure.microsoft.com/en-us/products/azure-sql/database

www.oracle.com/database

yaacomo.com

Technical documentation

docs.bangdb.com

cloud.google.com/bigquery/docs

docs.microsoft.com/en-us/azure/azure-sql

docs.oracle.com/en/database

Developer

Sachin Sinha, BangDB

Google

Microsoft

Oracle

Q2WEB GmbH

Initial release

2012

2010

1980

2009

Current release

BangDB 2.0, October 2021

V12

23c, September 2023

License

Commercial or Open Source

Open Source

BSD 3

commercial

restricted free version is 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

C, C++

C++

C and C++

Server operating systems

Linux

hosted

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

Android
Linux
Windows

Data scheme

schema-free

yes

Schemaless in JSON and XML columns

yes

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

yes

APIs and other access methods

Proprietary protocol
RESTful HTTP API

RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

JDBC
ODBC

Supported programming languages

C
C#
C++
Java
Python

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript (Node.js)
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

Transact SQL

PL/SQL

also stored procedures in Java possible

Triggers

yes, Notifications (with Streaming only)

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

none

Sharding, horizontal partitioning

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

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

yes, with always 3 replicas available

Multi-source replication
Source-replica 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

ACID

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'

yes

User concepts

Access control

yes (enterprise version only)

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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