DBMS > Lovefield vs. Microsoft Azure Cosmos DB vs. Netezza vs. Newts vs. Quasardb

System Properties Comparison Lovefield vs. Microsoft Azure Cosmos DB vs. Netezza vs. Newts vs. Quasardb

Editorial information provided by DB-Engines

Name

Description

Embeddable relational database for web apps written in pure JavaScript

Globally distributed, horizontally scalable, multi-model database service

Data warehouse and analytics appliance part of IBM PureSystems

Time Series DBMS based on Cassandra

Distributed, high-performance timeseries database

Primary database model

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Spatial DBMS

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

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	9.06
Rank	#46	Overall
	#29	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Website

google.github.io/lovefield

azure.microsoft.com/services/cosmos-db

www.ibm.com/products/netezza

opennms.github.io/newts

quasar.ai

Technical documentation

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

learn.microsoft.com/azure/cosmos-db

github.com/OpenNMS/newts/wiki

doc.quasar.ai/master

Developer

Google

Microsoft

IBM

OpenNMS Group

quasardb

Initial release

2014

2000

2014

2009

Current release

2.1.12, February 2017

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Apache 2.0

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

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

JavaScript

Java

C++

Server operating systems

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

tested with Chrome, Firefox, IE, Safari

hosted

Linux

included in appliance

Linux
OS X
Windows

BSD
Linux
OS X
Windows

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

JSON types

yes

integer and binary

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

All properties auto-indexed by default

yes

with tags

SQL

Support of SQL

SQL-like query language

via JavaScript builder pattern

SQL-like query language

yes

SQL-like query language

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
OLE DB

HTTP REST
Java API

HTTP API

Supported programming languages

JavaScript

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

C
C++
Fortran
Java
Lua
Perl
Python
R

Java

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

Server-side scripts

Stored procedures

JavaScript

yes

Triggers

Using read-only observers

JavaScript

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

based on Cassandra

Sharding

consistent hashing

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes

Implicit feature of the cloud service

Source-replica replication

selectable replication factor

based on Cassandra

Source-replica replication with selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

yes

by using LevelDB

In-memory capabilities

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

yes

using MemoryDB

yes

Transient mode

User concepts

Access control

Access rights can be defined down to the item level

Users with fine-grained authorization concept

Cryptographically strong user authentication and audit trail

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