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

Document store
Spatial DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	3.51
Rank	#86	Overall
	#46	Relational DBMS

Website

www.datomic.com

azure.microsoft.com/services/data-explorer

www.mysql.com

www.progress.com/openedge

Technical documentation

docs.datomic.com

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

dev.mysql.com/doc

documentation.progress.com/output/ua/OpenEdge_latest

Developer

Cognitect

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Progress Software Corporation

Initial release

2012

2019

1995

1984

Current release

1.0.6735, June 2023

cloud service with continuous releases

8.3.0, January 2024

OpenEdge 12.2, March 2020

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java, Clojure

C and C++

Server operating systems

All OS with a Java VM

hosted

FreeBSD
Linux
OS X
Solaris
Windows

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

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

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

Kusto Query Language (KQL), SQL subset

yes

with proprietary extensions

yes

close to SQL 92

APIs and other access methods

RESTful HTTP API

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

ADO.NET
JDBC
ODBC
Proprietary native API

JDBC
ODBC

Supported programming languages

Clojure
Java

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

Progress proprietary ABL (Advanced Business Language)

Server-side scripts

Stored procedures

yes

Transaction Functions

Yes, possible languages: KQL, Python, R

yes

proprietary syntax

yes

Triggers

By using transaction functions

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

horizontal partitioning

since Version 11.4

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

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

Multi-source replication
Source-replica replication

Source-replica replication

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

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

ACID

not for MyISAM storage engine

ACID

Concurrency

Support for concurrent manipulation of data

yes

table locks or row locks depending on storage engine

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

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

yes

recommended only for testing and development

yes

User concepts

Access control

Azure Active Directory Authentication

Users with fine-grained authorization concept

no user groups or roles

Users and groups

More information provided by the system vendor

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