DBMS > Google BigQuery vs. Hypertable vs. Netezza vs. Trafodion

System Properties Comparison Google BigQuery vs. Hypertable vs. Netezza vs. Trafodion

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Editorial information provided by DB-Engines

Name

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

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

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

Data warehouse and analytics appliance part of IBM PureSystems

Transactional SQL-on-Hadoop DBMS

Primary database model

Relational DBMS

Wide column store

Relational DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Website

cloud.google.com/bigquery

www.ibm.com/products/netezza

trafodion.apache.org

Technical documentation

cloud.google.com/bigquery/docs

trafodion.apache.org/documentation.html

Developer

Google

Hypertable Inc.

IBM

Apache Software Foundation, originally developed by HP

Initial release

2010

2009

2000

2014

Current release

0.9.8.11, March 2016

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

GNU version 3. Commercial license available

commercial

Open Source

Apache 2.0

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++

C++, Java

Server operating systems

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux

included in appliance

Linux

Data scheme

yes

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

restricted

only exact value or prefix value scans

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

C++ API
Thrift

JDBC
ODBC
OLE DB

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Java Stored Procedures

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor on file system level

Source-replica replication

yes, via HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via user defined functions and HBase

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.

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Users with fine-grained authorization concept

fine grained access rights according to SQL-standard

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