DBMS > Amazon Redshift vs. BaseX vs. Datomic vs. Microsoft Azure Data Explorer

System Properties Comparison Amazon Redshift vs. BaseX vs. Datomic vs. Microsoft Azure Data Explorer

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Light-weight Native XML DBMS with support for XQuery 3.0 and interactive GUI.

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Fully managed big data interactive analytics platform

Primary database model

Relational DBMS

Native XML DBMS

Relational DBMS

column oriented

Secondary database models

Document store

If a column is of type dynamic docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types/dynamic then it's possible to add arbitrary JSON documents in this cell
Event Store

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	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.84
Rank	#135	Overall
	#4	Native XML DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Website

aws.amazon.com/redshift

basex.org

www.datomic.com

azure.microsoft.com/services/data-explorer

Technical documentation

docs.aws.amazon.com/redshift

docs.basex.org

docs.datomic.com

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

Developer

Amazon (based on PostgreSQL)

BaseX GmbH

Cognitect

Microsoft

Initial release

2012

2007

2012

2019

Current release

11.0, June 2024

1.0.7075, December 2023

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

BSD license

commercial

limited edition free

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

Java

Java, Clojure

Server operating systems

hosted

Linux
OS X
Windows

All OS with a Java VM

hosted

Data scheme

yes

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

Typing

predefined data types such as float or date

yes

XQuery supports types

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

restricted

yes

all fields are automatically indexed

SQL

Support of SQL

yes

does not fully support an SQL-standard

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC

Java API
RESTful HTTP API
RESTXQ
WebDAV
XML:DB
XQJ

RESTful HTTP API

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

Supported programming languages

All languages supporting JDBC/ODBC

Actionscript
C
C#
Haskell
Java
JavaScript

Node.js
Lisp
Perl
PHP
Python
Qt
Rebol
Ruby
Scala
Visual Basic

Clojure
Java

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

Server-side scripts

Stored procedures

user defined functions

in Python

yes

Transaction Functions

Yes, possible languages: KQL, Python, R

Triggers

yes

via events

By using transaction functions

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

But extensive use of caching in the application peers

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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.

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

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

multiple readers, single writer

ACID

Concurrency

Support for concurrent manipulation of data

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

User concepts

Access control

fine grained access rights according to SQL-standard

Users with fine-grained authorization concept on 4 levels

Azure Active Directory Authentication

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