DBMS > Google BigQuery vs. Microsoft Azure Cosmos DB vs. Oracle Rdb vs. SiriDB vs. Warp 10

System Properties Comparison Google BigQuery vs. Microsoft Azure Cosmos DB vs. Oracle Rdb vs. SiriDB vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Globally distributed, horizontally scalable, multi-model database service

Open Source Time Series DBMS

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	1.14
Rank	#178	Overall
	#80	Relational DBMS

Score	0.07
Rank	#378	Overall
	#42	Time Series DBMS

Score	0.14
Rank	#344	Overall
	#32	Time Series DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

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

siridb.com

www.warp10.io

Technical documentation

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

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

docs.siridb.com

www.warp10.io/content/02_Getting_started

Developer

Google

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Cesbit

SenX

Initial release

2010

2014

1984

2017

2015

Current release

7.4.1.1, 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

Apache License 2.0

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

Java

Server operating systems

hosted

HP Open VMS

Linux

Linux
OS X
Windows

Data scheme

yes

schema-free

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

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

Numeric data

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

HTTP API

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

yes

WarpScript

Triggers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

based on HBase

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

Multi-item ACID transactions with snapshot isolation within a partition

yes, on a single node

Concurrency

Support for concurrent manipulation of data

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights can be defined down to the item level

simple rights management via user accounts

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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