DBMS > IBM Cloudant vs. Kinetica vs. Microsoft Azure Synapse Analytics

System Properties Comparison IBM Cloudant vs. Kinetica vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

Database as a Service offering based on Apache CouchDB

Fully vectorized database across both GPUs and CPUs

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	2.73
Rank	#108	Overall
	#20	Document stores

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Website

www.ibm.com/products/cloudant

www.kinetica.com

azure.microsoft.com/services/synapse-analytics

Technical documentation

cloud.ibm.com/docs/Cloudant

docs.kinetica.com

docs.microsoft.com/azure/synapse-analytics

Developer

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Kinetica

Microsoft

Initial release

2010

2012

2016

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

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

Erlang

C, C++

C++

Server operating systems

hosted

Linux

hosted

Data scheme

schema-free

yes

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

C#
Java
JavaScript
Objective-C
PHP
Ruby

C++
Java
JavaScript (Node.js)
Python

C#
Java
PHP

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

user defined functions

Transact SQL

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

Source-replica replication

yes

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 or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

atomic operations within a document possible

ACID

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

User concepts

Access control

Access rights for users can be defined per database

Access rights for users and roles on table level

yes

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