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

Time Series DBMS

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

Score	0.22
Rank	#312	Overall
	#21	Search engines

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

manticoresearch.com

azure.microsoft.com/services/data-explorer

spark.apache.org/sql

www.sqlite.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

manual.manticoresearch.com

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

spark.apache.org/docs/latest/sql-programming-guide.html

www.sqlite.org/docs.html

Developer

Manticore Software

Microsoft

Apache Software Foundation

Dwayne Richard Hipp

Alex Kashirin

Initial release

2017

2019

2014

2000

2020

Current release

6.0, February 2023

cloud service with continuous releases

3.5.0 ( 2.13), September 2023

3.45.3 (15 April 2024), April 2024

0.5, April 2021

License

Commercial or Open Source

Open Source

GPL version 2

commercial

Open Source

Apache 2.0

Open Source

Public Domain

Open Source

GPL V3

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++

Scala

C++

Server operating systems

FreeBSD
Linux
macOS
Windows

hosted

Linux
OS X
Windows

server-less

Linux

Data scheme

Fixed schema

Fixed schema with schema-less datatypes (dynamic)

yes

dynamic column types

schema-free

Typing

predefined data types such as float or date

Int, Bigint, Float, Timestamp, Bit, Int array, Bigint array, JSON, Boolean

yes

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

yes

not rigid because of 'dynamic typing' concept.

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.

Can index from XML

yes

Secondary indexes

yes

full-text index on all search fields

all fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

SQL-like query language

APIs and other access methods

Binary API
RESTful HTTP/JSON API
RESTful HTTP/SQL API
SQL over MySQL

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

JDBC
ODBC

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Proprietary protocol
Thrift

Supported programming languages

Elixir
Go
Java
JavaScript (Node.js)
Perl
PHP
Python

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

Java
Python
R
Scala

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

C++

Server-side scripts

Stored procedures

user defined functions

Yes, possible languages: KQL, Python, R

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Partitioning is done manually, search queries against distributed index is supported

Sharding

Implicit feature of the cloud service

yes, utilizing Spark Core

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Synchronous replication based on Galera library

yes

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

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
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

isolated transactions for atomic changes and binary logging for safe writes

ACID

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

The original contents of fields are not stored in the Manticore index.

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

Azure Active Directory Authentication

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