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
Graph DBMS

with Oracle Spatial and Graph
RDF store

with Oracle Spatial and Graph
Spatial DBMS

with Oracle Spatial and Graph
Vector DBMS

since Oracle 23

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Trend Chart

Score	5.16
Rank	#69	Overall
	#37	Relational DBMS

Score	1234.27
Rank	#1	Overall
	#1	Relational DBMS

Score	3.27
Rank	#97	Overall
	#16	Document stores
	#6	Graph DBMS
	#15	Key-value stores

Score	6.03
Rank	#60	Overall
	#6	Search engines

Website

azure.microsoft.com/services/data-explorer

www.oracle.com/database

orientdb.org

sphinxsearch.com

Technical documentation

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

docs.oracle.com/en/database

www.orientdb.com/docs/last/index.html

sphinxsearch.com/docs

Developer

Microsoft

Oracle

OrientDB LTD; CallidusCloud; SAP

Sphinx Technologies Inc.

Initial release

2019

1980

2010

2001

Current release

cloud service with continuous releases

23c, September 2023

3.2.29, March 2024

3.5.1, February 2023

License

Commercial or Open Source

commercial

restricted free version is available

Open Source

Apache version 2

Open Source

GPL version 2, commercial licence available

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++

Java

C++

Server operating systems

hosted

AIX
HP-UX
Linux
OS X
Solaris
Windows
z/OS

All OS with a Java JDK (>= JDK 6)

FreeBSD
Linux
NetBSD
OS X
Solaris
Windows

Data scheme

Fixed schema with schema-less datatypes (dynamic)

yes

Schemaless in JSON and XML columns

schema-free

Schema can be enforced for whole record ("schema-full") or for some fields only ("schema-hybrid")

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

all fields are automatically indexed

yes

full-text index on all search fields

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

yes

with proprietary extensions

SQL-like query language, no joins

SQL-like query language (SphinxQL)

APIs and other access methods

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Tinkerpop technology stack with Blueprints, Gremlin, Pipes
Java API
RESTful HTTP/JSON API

Proprietary protocol

Supported programming languages

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

C
C#
C++
Clojure
Cobol
Delphi
Eiffel
Erlang
Fortran
Groovy
Haskell
Java
JavaScript
Lisp
Objective C
OCaml
Perl
PHP
Python
R
Ruby
Scala
Tcl
Visual Basic

.Net
C
C#
C++
Clojure
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby
Scala

C++

unofficial client library
Java
Perl

unofficial client library
PHP
Python
Ruby

unofficial client library

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

PL/SQL

also stored procedures in Java possible

Java, Javascript

Triggers

yes

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

yes

Hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding, horizontal partitioning

Sharding

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

Multi-source replication
Source-replica replication

Multi-source replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

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

can be realized in PL/SQL

could be achieved with distributed queries

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

relationship in graphs

Transaction concepts

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

ACID

isolation level can be parameterized

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

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

In-memory capabilities

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

yes

Version 12c introduced the new option 'Oracle Database In-Memory'

User concepts

Access control

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

Access rights for users and roles; record level security configurable

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