DBMS > Amazon Redshift vs. DataFS vs. Google Cloud Bigtable vs. Graph Engine vs. Hypertable

System Properties Comparison Amazon Redshift vs. DataFS vs. Google Cloud Bigtable vs. Graph Engine vs. Hypertable

Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

Description

Large scale data warehouse service for use with business intelligence tools

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

An open source BigTable implementation based on distributed file systems such as Hadoop

Primary database model

Relational DBMS

Object oriented DBMS

Key-value store
Wide column store

Graph DBMS
Key-value store

Wide column store

Secondary database models

Graph DBMS

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

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

Score	0.61
Rank	#240	Overall
	#21	Graph DBMS
	#35	Key-value stores

Website

aws.amazon.com/redshift

newdatabase.com

cloud.google.com/bigtable

www.graphengine.io

Technical documentation

docs.aws.amazon.com/redshift

dev.mobiland.com/Overview.xsp

cloud.google.com/bigtable/docs

www.graphengine.io/docs/manual

Developer

Amazon (based on PostgreSQL)

Mobiland AG

Google

Microsoft

Hypertable Inc.

Initial release

2012

2018

2015

2010

2009

Current release

1.1.263, October 2022

0.9.8.11, March 2016

License

Commercial or Open Source

commercial

Open Source

MIT License

Open Source

GNU version 3. Commercial license available

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

.NET and C

C++

Server operating systems

hosted

Windows

hosted

.NET

Linux
OS X
Windows

an inofficial Windows port is available

Data scheme

yes

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

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.

Secondary indexes

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes

does not fully support an SQL-standard

APIs and other access methods

JDBC
ODBC

.NET Client API
Proprietary client DLL
WinRT client

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

RESTful HTTP API

C++ API
Thrift

Supported programming languages

All languages supporting JDBC/ODBC

.Net
C
C#
C++
VB.Net

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

C#
C++
F#
Visual Basic

C++
Java
Perl
PHP
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Triggers

no, except callback-events from server when changes happened

Partitioning methods

Methods for storing different data on different nodes

Sharding

Proprietary Sharding system

Sharding

horizontal partitioning

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

selectable replication factor on file system level

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

ACID

Atomic single-row operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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

fine grained access rights according to SQL-standard

Windows-Profile

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

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