DBMS > Drizzle vs. IBM Cloudant vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer

System Properties Comparison Drizzle vs. IBM Cloudant vs. Kinetica vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Database as a Service offering based on Apache CouchDB

Fully vectorized database across both GPUs and CPUs

Globally distributed, horizontally scalable, multi-model database service

Fully managed big data interactive analytics platform

Primary database model

Relational DBMS

Document store

Relational DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

column oriented

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

this is the general usage pattern at Microsoft. Billing, Logs, Telemetry events are stored in ADX and the state of an individual entity is defined by the arg_max(timestamps)
Spatial DBMS
Search engine

support for complex search expressions docs.microsoft.com/en-us/azure/kusto/query/parseoperator FTS, Geospatial docs.microsoft.com/en-us/azure/kusto/query/geo-point-to-geohash-function distributed search -> ADX acts as a distributed search engine
Time Series DBMS

see docs.microsoft.com/en-us/azure/data-explorer/time-series-analysis

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

Score	2.68
Rank	#106	Overall
	#20	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

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

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.ibm.com/products/cloudant

www.kinetica.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/data-explorer

Technical documentation

cloud.ibm.com/docs/Cloudant

docs.kinetica.com

learn.microsoft.com/azure/cosmos-db

docs.microsoft.com/en-us/azure/data-explorer

Developer

Drizzle project, originally started by Brian Aker

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Kinetica

Microsoft

Initial release

2008

2010

2012

2014

2019

Current release

7.2.4, September 2012

7.1, August 2021

cloud service with continuous releases

License

Commercial or Open Source

Open Source

GNU GPL

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

C++

Erlang

C, C++

Server operating systems

FreeBSD
Linux
OS X

hosted

Linux

hosted

Data scheme

yes

schema-free

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

JSON types

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

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

All properties auto-indexed by default

all fields are automatically indexed

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

SQL-like query language

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

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

Microsoft SQL Server communication protocol (MS-TDS)
RESTful HTTP API

Supported programming languages

C
C++
Java
PHP

C#
Java
JavaScript
Objective-C
PHP
Ruby

C++
Java
JavaScript (Node.js)
Python

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

.Net
Go
Java
JavaScript (Node.js)
PowerShell
Python
R

Server-side scripts

Stored procedures

View functions (Map-Reduce) in JavaScript

user defined functions

JavaScript

Yes, possible languages: KQL, Python, R

Triggers

hooks for callbacks inside the server can be used.

yes

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

JavaScript

yes

see docs.microsoft.com/en-us/azure/kusto/management/updatepolicy

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

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

yes

Implicit feature of the cloud service. Replication either local, cross-facility or geo-redundant.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Spark connector (open source): github.com/Azure/azure-kusto-spark

Consistency concepts

Methods to ensure consistency in a distributed system

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

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

atomic operations within a document possible

Multi-item ACID transactions with snapshot isolation within a partition

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

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users can be defined per database

Access rights for users and roles on table level

Access rights can be defined down to the item level

Azure Active Directory Authentication

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