DBMS > Hypertable vs. Netezza vs. Oracle NoSQL vs. RavenDB vs. Snowflake

System Properties Comparison Hypertable vs. Netezza vs. Oracle NoSQL vs. RavenDB vs. Snowflake

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

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

Data warehouse and analytics appliance part of IBM PureSystems

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Open Source Operational and Transactional Enterprise NoSQL Document Database

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Wide column store

Relational DBMS

Document store
Key-value store
Relational DBMS

Document store

Relational DBMS

Secondary database models

Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	2.92
Rank	#101	Overall
	#18	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

www.ibm.com/products/netezza

www.oracle.com/database/nosql/technologies/nosql

ravendb.net

www.snowflake.com

Technical documentation

docs.oracle.com/en/database/other-databases/nosql-database/index.html

ravendb.net/docs

docs.snowflake.net/manuals/index.html

Developer

Hypertable Inc.

IBM

Oracle

Hibernating Rhinos

Snowflake Computing Inc.

Initial release

2009

2000

2011

2010

2014

Current release

0.9.8.11, March 2016

23.3, December 2023

5.4, July 2022

License

Commercial or Open Source

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

AGPL version 3, commercial license available

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

Java

Server operating systems

Linux
OS X
Windows

an inofficial Windows port is available

Linux

included in appliance

Linux
Solaris SPARC/x86

Linux
macOS
Raspberry Pi
Windows

hosted

Data scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

Typing

predefined data types such as float or date

yes

optional

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language (RQL)

yes

APIs and other access methods

C++ API
Thrift

JDBC
ODBC
OLE DB

RESTful HTTP API

.NET Client API
F# Client API
Go Client API
Java Client API
NodeJS Client API
PHP Client API
Python Client API
RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
Perl
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

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

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

Source-replica replication

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Default ACID transactions on the local node (eventually consistent across the cluster). Atomic operations with cluster-wide ACID transactions. Eventual consistency for indexes and full-text search indexes.

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID, Cluster-wide transaction available

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

off heap cache

User concepts

Access control

Users with fine-grained authorization concept

Access rights for users and roles

Authorization levels configured per client per database

Users with fine-grained authorization concept, user roles and pluggable authentication

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