DBMS > Cassandra vs. Hive vs. Prometheus vs. Quasardb vs. SiteWhere

System Properties Comparison Cassandra vs. Hive vs. Prometheus vs. Quasardb vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Wide-column store based on ideas of BigTable and DynamoDB

Optimized for write access

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

Open-source Time Series DBMS and monitoring system

Distributed, high-performance timeseries database

M2M integration platform for persisting/querying time series data

Primary database model

Wide column store

Relational DBMS

Time Series DBMS

Secondary database models

Vector DBMS

starting with release V5

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

Score	101.89
Rank	#12	Overall
	#1	Wide column stores

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	8.42
Rank	#47	Overall
	#2	Time Series DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

github.com/sitewhere/sitewhere

Technical documentation

cassandra.apache.org/doc/latest

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

prometheus.io/docs

doc.quasar.ai/master

sitewhere1.sitewhere.io/index.html

Developer

Apache Software Foundation

Apache top level project, originally developped by Facebook

Apache Software Foundation

initially developed by Facebook

quasardb

SiteWhere

Initial release

2008

2012

2015

2009

2010

Current release

4.1.3, July 2023

3.1.3, April 2022

3.14.1, January 2024

License

Commercial or Open Source

Open Source

Apache version 2

Open Source

Apache Version 2

Open Source

Apache 2.0

commercial

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

Open Source

Common Public Attribution License Version 1.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Astra DB: Multi-cloud DBaaS built on Apache Cassandra.

Implementation language

Java

C++

Java

Server operating systems

BSD
Linux
OS X
Windows

All OS with a Java VM

Linux
Windows

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

predefined scheme

Typing

predefined data types such as float or date

yes

Numeric data only

yes

integer and binary

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.

Import of XML data possible

Secondary indexes

restricted

only equality queries, not always the best performing solution

yes

with tags

SQL

Support of SQL

SQL-like SELECT, DML and DDL statements (CQL)

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

Proprietary protocol

CQL (Cassandra Query Language, an SQL-like language)
Thrift

JDBC
ODBC
Thrift

RESTful HTTP/JSON API

HTTP API

HTTP REST

Supported programming languages

C#
C++
Clojure
Erlang
Go
Haskell
Java
JavaScript

Node.js
Perl
PHP
Python
Ruby
Scala

C++
Java
PHP
Python

.Net
C++
Go
Haskell
Java
JavaScript (Node.js)
Python
Ruby

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

no "single point of failure"

Sharding

consistent hashing

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Representation of geographical distribution of servers is possible

selectable replication factor

yes

by Federation

Source-replica replication with selectable replication factor

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be individually decided for each write operation

Eventual Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Atomicity and isolation are supported for single operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

User concepts

Access control

Access rights for users can be defined per object

Access rights for users, groups and roles

Cryptographically strong user authentication and audit trail

Users with fine-grained authorization concept

More information provided by the system vendor

Specific characteristics

Apache Cassandra is the leading NoSQL, distributed database management system, well...
» more

Competitive advantages

No single point of failure ensures 100% availability . Operational simplicity for...
» more

Typical application scenarios

Internet of Things (IOT), fraud detection applications, recommendation engines, product...
» more

Key customers

Apple, Netflix, Uber, ING,, Intuit,Fidelity, NY Times, Outbrain, BazaarVoice, Best...
» more

Market metrics

Cassandra is used by 40% of the Fortune 100.
» more

Licensing and pricing models

Apache licenseÂ Pricing for commercial distributions provided by DataStax and available...
» more

News

Astra Vectorize: Generate Vector Embeddings with 1 Line of Code
22 May 2024

GitHub Copilot + DataStax Astra DB: Build GenAI Apps 100x Faster
21 May 2024

Empowering On-Premises Deployments with Generative AI: Introducing Vector Search for a Self-Managed Modern Architecture
15 May 2024

Tips and Tricks for the DataStax Astra CLI
7 May 2024

How to Build a Crystal Image Search App with Vector Search
25 April 2024

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.