DBMS > Amazon SimpleDB vs. Google Cloud Bigtable vs. H2GIS vs. Microsoft SQL Server vs. SWC-DB

System Properties Comparison Amazon SimpleDB vs. Google Cloud Bigtable vs. H2GIS vs. Microsoft SQL Server vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Hosted simple database service by Amazon, with the data stored in the Amazon Cloud.

There is an unrelated product called SimpleDB developed by Edward Sciore

Google's NoSQL Big Data database service. It's the same database that powers many core Google services, including Search, Analytics, Maps, and Gmail.

Spatial extension of H2

Microsofts flagship relational DBMS

A high performance, scalable Wide Column DBMS

Primary database model

Key-value store

Key-value store
Wide column store

Spatial DBMS

Relational DBMS

Wide column store

Secondary database models

Relational DBMS

Document store
Graph DBMS
Spatial DBMS

Time Series DBMS

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

Score	1.85
Rank	#138	Overall
	#24	Key-value stores

Score	3.26
Rank	#92	Overall
	#13	Key-value stores
	#8	Wide column stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

aws.amazon.com/simpledb

cloud.google.com/bigtable

www.h2gis.org

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

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.aws.amazon.com/simpledb

cloud.google.com/bigtable/docs

www.h2gis.org/docs/home

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

Developer

Amazon

Google

CNRS

Microsoft

Alex Kashirin

Initial release

2007

2015

2013

1989

2020

Current release

SQL Server 2022, November 2022

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

commercial

restricted free version is available

Open Source

GPL V3

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

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Implementation language

Java

C++

Server operating systems

hosted

Linux
Windows

Linux

Data scheme

schema-free

yes

schema-free

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

All columns are indexed automatically

yes

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

RESTful HTTP API

gRPC (using protocol buffers) API
HappyBase (Python library)
HBase compatible API (Java)

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

Proprietary protocol
Thrift

Supported programming languages

.Net
C
C++
Erlang
Java
PHP
Python
Ruby
Scala

C#
C++
Go
Java
JavaScript (Node.js)
Python

Java

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

C++

Server-side scripts

Stored procedures

yes

based on H2

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

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

Sharding

none

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

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Internal replication in Colossus, and regional replication between two clusters in different zones

yes

based on H2

yes, but depending on the SQL-Server Edition

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate consistency (for a single cluster), Eventual consistency (for two or more replicated clusters)

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Concurrent data updates can be detected by the application

Atomic single-row operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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 rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

yes

based on H2

fine grained access rights according to SQL-standard

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