DBMS > Amazon SimpleDB vs. Dolt vs. Google BigQuery vs. TigerGraph vs. TimesTen

System Properties Comparison Amazon SimpleDB vs. Dolt vs. Google BigQuery vs. TigerGraph vs. TimesTen

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

A MySQL compatible DBMS with Git-like versioning of data and schema

Large scale data warehouse service with append-only tables

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

In-Memory RDBMS compatible to Oracle

Primary database model

Secondary database models

Document store

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

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

Score	0.96
Rank	#193	Overall
	#90	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

aws.amazon.com/simpledb

github.com/dolthub/dolt
www.dolthub.com

cloud.google.com/bigquery

www.tigergraph.com

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

Technical documentation

docs.aws.amazon.com/simpledb

docs.dolthub.com

cloud.google.com/bigquery/docs

docs.tigergraph.com

docs.oracle.com/database/timesten-18.1

Developer

Amazon

DoltHub Inc

Google

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2007

2018

2010

2017

1998

Current release

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

commercial

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

C++

Server operating systems

hosted

Linux
macOS
Windows

hosted

Linux

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

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.

Secondary indexes

yes

All columns are indexed automatically

yes

SQL

Support of SQL

yes

SQL-like query language (GSQL)

yes

APIs and other access methods

RESTful HTTP API

CLI Client
HTTP REST

RESTful HTTP/JSON API

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

yes

currently in alpha release

user defined functions

in JavaScript

yes

PL/SQL

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding must be implemented in the application

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

A database can be cloned to multiple locations and be used there in isolation. Data/schema changes can be pushed/pulled explicitly between locations.

Multi-source replication
Source-replica replication

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

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

yes

Transaction concepts

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

Concurrent data updates can be detected by the application

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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)

Only one user is configurable, and must be specified in the config file at startup

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

Google Cloud Identity & Access Management (IAM)

Role-based access control

fine grained access rights according to SQL-standard

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