DBMS > Couchbase vs. Kinetica vs. Microsoft Azure SQL Database vs. Spark SQL vs. Warp 10

System Properties Comparison Couchbase vs. Kinetica vs. Microsoft Azure SQL Database vs. Spark SQL vs. Warp 10

Editorial information provided by DB-Engines

Name

Description

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

Fully vectorized database across both GPUs and CPUs

Database as a Service offering with high compatibility to Microsoft SQL Server

Spark SQL is a component on top of 'Spark Core' for structured data processing

TimeSeries DBMS specialized on timestamped geo data based on LevelDB or HBase

Primary database model

Document store

Relational DBMS

Time Series DBMS

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

Spatial DBMS
Time Series DBMS

Document store
Graph DBMS
Spatial DBMS

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

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	77.99
Rank	#16	Overall
	#11	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.07
Rank	#349	Overall
	#32	Time Series DBMS

Website

www.couchbase.com

www.kinetica.com

azure.microsoft.com/en-us/products/azure-sql/database

spark.apache.org/sql

www.warp10.io

Technical documentation

docs.couchbase.com

docs.kinetica.com

docs.microsoft.com/en-us/azure/azure-sql

spark.apache.org/docs/latest/sql-programming-guide.html

www.warp10.io/content/02_Getting_started

Developer

Couchbase, Inc.

Kinetica

Microsoft

Apache Software Foundation

SenX

Initial release

2011

2012

2010

2014

2015

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

7.1, August 2021

V12

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Open Source

Apache 2.0

Open Source

Apache License 2.0

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

C, C++, Go and Erlang

C, C++

C++

Scala

Java

Server operating systems

Linux
OS X
Windows

Linux

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

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++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

SQL-like DML and DDL statements

yes

SQL-like DML and DDL statements

APIs and other access methods

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

HTTP API
Jupyter
WebSocket

Supported programming languages

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

C++
Java
JavaScript (Node.js)
Python

.Net
C#
Java
JavaScript (Node.js)
PHP
Python
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

user defined functions

Transact SQL

yes

WarpScript

Triggers

yes

via the TAP protocol

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Automatic Sharding

Sharding

yes, utilizing Spark Core

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

including cross data center replication
Source-replica replication

Source-replica replication

yes, with always 3 replicas available

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

based on HBase

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

In-memory capabilities

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

yes

Ephemeral buckets

yes

GPU vRAM or System RAM

yes

User concepts

Access control

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

Mandatory use of cryptographic tokens, containing fine-grained authorizations

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