DBMS > Hive vs. Lovefield vs. Netezza vs. Snowflake

System Properties Comparison Hive vs. Lovefield vs. Netezza vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Embeddable relational database for web apps written in pure JavaScript

Data warehouse and analytics appliance part of IBM PureSystems

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

Primary database model

Relational DBMS

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Trend Chart

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

hive.apache.org

google.github.io/lovefield

www.ibm.com/products/netezza

www.snowflake.com

Technical documentation

cwiki.apache.org/confluence/display/Hive/Home

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

initially developed by Facebook

Google

IBM

Snowflake Computing Inc.

Initial release

2012

2014

2000

2014

Current release

3.1.3, April 2022

2.1.12, February 2017

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache 2.0

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

Java

JavaScript

Server operating systems

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Linux

included in appliance

hosted

Data scheme

yes

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

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

SQL-like DML and DDL statements

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

JDBC
ODBC
Thrift

JDBC
ODBC
OLE DB

CLI Client
JDBC
ODBC

Supported programming languages

C++
Java
PHP
Python

JavaScript

C
C++
Fortran
Java
Lua
Perl
Python
R

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

user defined functions

Triggers

Using read-only observers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

User concepts

Access control

Access rights for users, groups and roles

Users with fine-grained authorization concept

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

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