DBMS > HBase vs. Kinetica vs. Microsoft Azure Cosmos DB vs. VelocityDB

System Properties Comparison HBase vs. Kinetica vs. Microsoft Azure Cosmos DB vs. VelocityDB

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Editorial information provided by DB-Engines

Name

Description

Wide-column store based on Apache Hadoop and on concepts of BigTable

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

A .NET Object Database that can be embedded/distributed and extended to a graph data model (VelocityGraph)

Primary database model

Wide column store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Graph DBMS
Object oriented DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.11
Rank	#354	Overall
	#37	Graph DBMS
	#15	Object oriented DBMS

Website

hbase.apache.org

www.kinetica.com

azure.microsoft.com/services/cosmos-db

velocitydb.com

Technical documentation

hbase.apache.org/book.html

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

velocitydb.com/UserGuide

Developer

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Kinetica

Microsoft

VelocityDB Inc

Initial release

2008

2012

2014

2011

Current release

2.3.4, January 2021

7.1, August 2021

7.x

License

Commercial or Open Source

Open Source

Apache version 2

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

C, C++

Server operating systems

Linux
Unix
Windows

using Cygwin

Linux

hosted

Any that supports .NET

Data scheme

schema-free, schema definition possible

yes

schema-free

yes

Typing

predefined data types such as float or date

options to bring your own types, AVRO

yes

JSON types

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

All properties auto-indexed by default

yes

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language

APIs and other access methods

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
RESTful HTTP API

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

.Net

Supported programming languages

C
C#
C++
Groovy
Java
PHP
Python
Scala

C++
Java
JavaScript (Node.js)
Python

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

.Net

Server-side scripts

Stored procedures

yes

Coprocessors in Java

user defined functions

JavaScript

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

JavaScript

Callbacks are triggered when data changes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

Implicit feature of the cloud service

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Single row ACID (across millions of columns)

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

In-memory capabilities

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

yes

GPU vRAM or System RAM

yes

User concepts

Access control

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Access rights for users and roles on table level

Access rights can be defined down to the item level

Based on Windows Authentication

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