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

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

Score	2.47
Rank	#110	Overall
	#54	Relational DBMS

Score	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	2.01
Rank	#126	Overall
	#21	Key-value stores
	#3	Native XML DBMS

Score	41.02
Rank	#24	Overall
	#3	Search engines

Website

www.4d.com

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/data-explorer

www.oracle.com/database/technologies/related/berkeleydb.html

solr.apache.org

Technical documentation

developer.4d.com

learn.microsoft.com/azure/cosmos-db

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

docs.oracle.com/cd/E17076_05/html/index.html

solr.apache.org/resources.html

Developer

4D, Inc

Microsoft

Oracle

originally developed by Sleepycat, which was acquired by Oracle

Apache Software Foundation

Initial release

1984

2014

2019

1994

2006

Current release

v20, April 2023

cloud service with continuous releases

18.1.40, May 2020

9.6.1, May 2024

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

Apache Version 2

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, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

OS X
Windows

hosted

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

All OS with a Java VM

runs as a servlet in servlet container (e.g. Tomcat, Jetty is included)

Data scheme

yes

schema-free

Fixed schema with schema-less datatypes (dynamic)

schema-free

yes

Dynamic Fields enables on-the-fly addition of new fields

Typing

predefined data types such as float or date

yes

JSON types

yes

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

yes

supports customizable data types and automatic typing

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

only with the Berkeley DB XML edition

yes

Secondary indexes

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

All search fields are automatically indexed

SQL

Support of SQL

yes

close to SQL 92

SQL-like query language

Kusto Query Language (KQL), SQL subset

yes

SQL interfaced based on SQLite is available

Solr Parallel SQL Interface

APIs and other access methods

ODBC
RESTful HTTP API

by using 4D Mobile
SOAP webservices

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

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

Java API
RESTful HTTP/JSON API

Supported programming languages

4D proprietary IDE
PHP

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

.Net
Erlang
Java
JavaScript
any language that supports sockets and either XML or JSON
Perl
PHP
Python
Ruby
Scala

Server-side scripts

Stored procedures

yes

JavaScript

Yes, possible languages: KQL, Python, R

Java plugins

Triggers

yes

JavaScript

yes

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

yes

only for the SQL API

yes

User configurable commands triggered on index changes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes

Implicit feature of the cloud service

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

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

spark-solr: github.com/lucidworks/spark-solr and streaming expressions to reduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Multi-item ACID transactions with snapshot isolation within a partition

ACID

optimistic locking

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

Users and groups

Access rights can be defined down to the item level

Azure Active Directory Authentication

yes

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