DBMS > Google BigQuery vs. Heroic vs. Netezza

System Properties Comparison Google BigQuery vs. Heroic vs. Netezza

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

Data warehouse and analytics appliance part of IBM PureSystems

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.57
Rank	#250	Overall
	#21	Time Series DBMS

Score	10.18
Rank	#46	Overall
	#29	Relational DBMS

Website

cloud.google.com/bigquery

github.com/spotify/heroic

www.ibm.com/products/netezza

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

Developer

Google

Spotify

IBM

Initial release

2010

2014

2000

License

Commercial or Open Source

commercial

Open Source

Apache 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

Java

Server operating systems

hosted

Linux

included in appliance

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

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

RESTful HTTP/JSON API

HQL (Heroic Query Language, a JSON-based language)
HTTP API

JDBC
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C++
Fortran
Java
Lua
Perl
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

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

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.