DBMS > Google BigQuery vs. Microsoft SQL Server vs. SAP HANA vs. SAP SQL Anywhere vs. SiteWhere

System Properties Comparison Google BigQuery vs. Microsoft SQL Server vs. SAP HANA vs. SAP SQL Anywhere vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Microsofts flagship relational DBMS

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

RDBMS database and synchronization technologies for server, desktop, remote office, and mobile environments

M2M integration platform for persisting/querying time series data

Primary database model

Relational DBMS

Time Series 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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	821.56
Rank	#3	Overall
	#3	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Score	3.95
Rank	#80	Overall
	#42	Relational DBMS

Score	0.06
Rank	#383	Overall
	#43	Time Series DBMS

Website

cloud.google.com/bigquery

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

www.sap.com/products/hana.html

www.sap.com/products/technology-platform/sql-anywhere.html

github.com/sitewhere/sitewhere

Technical documentation

cloud.google.com/bigquery/docs

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

help.sap.com/hana

help.sap.com/docs/SAP_SQL_Anywhere

sitewhere1.sitewhere.io/index.html

Developer

Google

Microsoft

SAP

formerly Sybase

SiteWhere

Initial release

2010

1989

2010

1992

2010

Current release

SQL Server 2022, November 2022

2.0 SPS07 (April 4, 2023), April 2023

17, July 2015

License

Commercial or Open Source

commercial

restricted free version is available

commercial

Open Source

Common Public Attribution License Version 1.0

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

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Implementation language

C++

Java

Server operating systems

hosted

Linux
Windows

Appliance or cloud-service

AIX
HP-UX
Linux
OS X
Solaris
Windows

Linux
OS X
Windows

Data scheme

yes

predefined scheme

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

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

JDBC
ODBC

ADO.NET
HTTP API
JDBC
ODBC

HTTP REST

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

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

SQLScript, R

yes, in C/C++, Java, .Net or Perl

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

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

yes

none

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes, but depending on the SQL-Server Edition

yes

Source-replica replication

Database mirroring

selectable replication factor

based on HBase

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

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)

fine grained access rights according to SQL-standard

yes

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept

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