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	0.27
Rank	#309	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	18.08
Rank	#32	Overall
	#4	Key-value stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.74
Rank	#224	Overall
	#103	Relational DBMS

Website

www.ibm.com/products/db2-event-store

www.memcached.org

azure.microsoft.com/services/data-explorer

www.snowflake.com

sqream.com

Technical documentation

www.ibm.com/docs/en/db2-event-store

github.com/memcached/memcached/wiki

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

docs.snowflake.net/manuals/index.html

docs.sqream.com

Developer

IBM

Danga Interactive

originally developed by Brad Fitzpatrick for LiveJournal

Microsoft

Snowflake Computing Inc.

SQream Technologies

Initial release

2017

2003

2019

2014

2017

Current release

2.0

1.6.27, May 2024

cloud service with continuous releases

2022.1.6, December 2022

License

Commercial or Open Source

commercial

free developer edition available

Open Source

BSD license

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 and C++

C++, CUDA, Haskell, Java, Scala

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

FreeBSD
Linux
OS X
Unix
Windows

hosted

Linux

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

yes

support of semi-structured data formats (JSON, XML, Avro)

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

yes, ANSI Standard SQL 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

all fields are automatically indexed

SQL

Support of SQL

yes

through the embedded Spark runtime

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

Proprietary protocol

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

CLI Client
JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.Net
C
C++
ColdFusion
Erlang
Java
Lisp
Lua
OCaml
Perl
PHP
Python
Ruby

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

JavaScript (Node.js)
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

user defined functions

user defined functions in Python

Triggers

yes

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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

yes

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Active-active shard replication

none

Repcached, a Memcached patch, provides this functionallity

yes

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

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

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

No - written data is immutable

yes

Durability

Support for making data persistent

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

yes

In-memory capabilities

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

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

using SASL (Simple Authentication and Security Layer) protocol

Azure Active Directory Authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

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.