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	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.86
Rank	#134	Overall
	#24	Key-value stores

Score	27.97
Rank	#26	Overall
	#2	Wide column stores

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Website

impala.apache.org

geode.apache.org

hbase.apache.org

azure.microsoft.com/services/data-explorer

Technical documentation

impala.apache.org/impala-docs.html

geode.apache.org/docs

hbase.apache.org/book.html

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

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Microsoft

Initial release

2013

2002

2008

2019

Current release

4.1.0, June 2022

1.1, February 2017

2.3.4, January 2021

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2; commercial licenses available as Gemfire

Open Source

Apache version 2

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++

Java

Server operating systems

Linux

All OS with a Java VM

the JDK (8 or later) is also required

Linux
Unix
Windows

using Cygwin

hosted

Data scheme

yes

schema-free

schema-free, schema definition possible

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

yes

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

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like DML and DDL statements

SQL-like query language (OQL)

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

Java Client API
Memcached protocol
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

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

Supported programming languages

All languages supporting JDBC/ODBC

.Net
All JVM based languages
C++
Groovy
Java
Scala

C
C#
C++
Groovy
Java
PHP
Python
Scala

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

Coprocessors in Java

Yes, possible languages: KQL, Python, R

Triggers

yes

Cache Event Listeners

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Multi-source replication
Source-replica replication

yes

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

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency or Eventual Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes, on a single node

Single row ACID (across millions of columns)

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Access rights per client and object definable

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

Azure Active Directory Authentication

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

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