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

Spatial DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	2.66
Rank	#107	Overall
	#20	Document stores

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.kinetica.com

azure.microsoft.com/services/data-explorer

rethinkdb.com

www.sap.com/products/hana.html

Technical documentation

docs.kinetica.com

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

rethinkdb.com/docs

help.sap.com/hana

Developer

Kinetica

Microsoft

The Linux Foundation

since July 2017

SAP

Initial release

2012

2019

2009

2010

Current release

7.1, August 2021

cloud service with continuous releases

2.4.1, August 2020

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

C, C++

C++

Server operating systems

Linux

hosted

Linux
OS X
Windows

Appliance or cloud-service

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

yes

Typing

predefined data types such as float or date

yes

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

yes

string, binary, float, bool, date, geometry

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

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like DML and DDL statements

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

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

JDBC
ODBC

Supported programming languages

C++
Java
JavaScript (Node.js)
Python

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

community-supported driver
C#

community-supported driver
C++

community-supported driver
Clojure

community-supported driver
Dart

community-supported driver
Erlang

community-supported driver
Go

community-supported driver
Haskell

community-supported driver
Java

official driver
JavaScript (Node.js)

official driver
Lisp

community-supported driver
Lua

community-supported driver
Objective-C

community-supported driver
Perl

community-supported driver
PHP

community-supported driver
Python

official driver
Ruby

official driver
Scala

community-supported driver

Server-side scripts

Stored procedures

user defined functions

Yes, possible languages: KQL, Python, R

SQLScript, R

Triggers

yes

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

yes

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

Client-side triggers through changefeeds

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

range based

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

Atomic single-document operations

ACID

Concurrency

Support for concurrent manipulation of data

yes

MVCC based

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 rights for users and roles on table level

Azure Active Directory Authentication

yes

users and table-level permissions

yes

More information provided by the system vendor

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.