DBMS > Google BigQuery vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. SwayDB

System Properties Comparison Google BigQuery vs. Microsoft Azure Cosmos DB vs. Microsoft SQL Server vs. SwayDB

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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

Microsofts flagship relational DBMS

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Key-value store

Secondary database models

Spatial DBMS

Document store
Graph DBMS
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	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	0.04
Rank	#387	Overall
	#61	Key-value stores

Website

cloud.google.com/bigquery

azure.microsoft.com/services/cosmos-db

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

swaydb.simer.au

Technical documentation

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

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

Developer

Google

Microsoft

Simer Plaha

Initial release

2010

2014

1989

2018

Current release

SQL Server 2022, November 2022

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

GNU Affero GPL V3.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

C++

Scala

Server operating systems

hosted

Linux
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

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

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

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

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

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

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

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

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

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, but depending on the SQL-Server Edition

none

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

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

ACID

Atomic execution of operations

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

fine grained access rights according to SQL-standard

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