DBMS > Google BigQuery vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. SAP HANA

System Properties Comparison Google BigQuery vs. Microsoft Azure Table Storage vs. Microsoft SQL Server vs. SAP HANA

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A Wide Column Store for rapid development using massive semi-structured datasets

Microsofts flagship relational DBMS

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Relational DBMS

Wide column store

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	4.92
Rank	#73	Overall
	#6	Wide column stores

Score	829.80
Rank	#3	Overall
	#3	Relational DBMS

Score	45.84
Rank	#22	Overall
	#16	Relational DBMS

Website

cloud.google.com/bigquery

azure.microsoft.com/en-us/services/storage/tables

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

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/bigquery/docs

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

help.sap.com/hana

Developer

Google

Microsoft

SAP

Initial release

2010

2012

1989

2010

Current release

SQL Server 2022, November 2022

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Linux
Windows

Appliance or cloud-service

Data scheme

yes

schema-free

yes

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

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

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

JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
C++
Java
JavaScript (Node.js)
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

SQLScript, R

Triggers

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

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

yes, but depending on the SQL-Server Edition

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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

optimistic locking

ACID

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 based on private key authentication or shared access signatures

fine grained access rights according to SQL-standard

yes

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