DBMS > Google BigQuery vs. InterSystems Caché vs. Netezza vs. Ultipa

System Properties Comparison Google BigQuery vs. InterSystems Caché vs. Netezza vs. Ultipa

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Editorial information provided by DB-Engines

Name

Caché is a deprecated database engine which is substituted with InterSystems IRIS. It therefore is removed from the DB-Engines Ranking.

Description

Large scale data warehouse service with append-only tables

A multi-model DBMS and application server

Data warehouse and analytics appliance part of IBM PureSystems

High performance Graph DBMS supporting HTAP high availability cluster deployment

Primary database model

Relational DBMS

Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

Graph DBMS

Secondary database models

Document store

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	8.59
Rank	#45	Overall
	#29	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

cloud.google.com/bigquery

www.intersystems.com/products/cache

www.ibm.com/products/netezza

www.ultipa.com

Technical documentation

cloud.google.com/bigquery/docs

docs.intersystems.com

www.ultipa.com/document

Developer

Google

InterSystems

IBM

Ultipa

Initial release

2010

1997

2000

2019

Current release

2018.1.4, May 2020

License

Commercial or Open Source

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Server operating systems

hosted

AIX
HP Open VMS
HP-UX
Linux
OS X
Solaris
Windows

Linux

included in appliance

Data scheme

yes

depending on used data model

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.

yes

Secondary indexes

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

.NET Client API
JDBC
ODBC
RESTful HTTP API

JDBC
ODBC
OLE DB

RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java

C
C++
Fortran
Java
Lua
Perl
Python
R

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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.

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

Users with fine-grained authorization concept

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