DBMS > Elasticsearch vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer vs. Stardog

System Properties Comparison Elasticsearch vs. Microsoft Azure Cosmos DB vs. Microsoft Azure Data Explorer vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

A distributed, RESTful modern search and analytics engine based on Apache Lucene

Elasticsearch lets you perform and combine many types of searches such as structured, unstructured, geo, and metric

Globally distributed, horizontally scalable, multi-model database service

Fully managed big data interactive analytics platform

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Search engine

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

column oriented

Graph DBMS
RDF store

Secondary database models

Document store
Spatial DBMS
Vector DBMS

Spatial DBMS

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	132.83
Rank	#7	Overall
	#1	Search engines

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	2.07
Rank	#122	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

www.elastic.co/elasticsearch

azure.microsoft.com/services/cosmos-db

azure.microsoft.com/services/data-explorer

www.stardog.com

Technical documentation

www.elastic.co/guide/en/elasticsearch/reference/current/index.html

learn.microsoft.com/azure/cosmos-db

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

docs.stardog.com

Developer

Elastic

Microsoft

Stardog-Union

Initial release

2010

2014

2019

2010

Current release

8.6, January 2023

cloud service with continuous releases

7.3.0, May 2020

License

Commercial or Open Source

Open Source

Elastic License

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Server operating systems

All OS with a Java VM

hosted

Linux
macOS
Windows

Data scheme

schema-free

Flexible type definitions. Once a type is defined, it is persistent

schema-free

Fixed schema with schema-less datatypes (dynamic)

schema-free and OWL/RDFS-schema support

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

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

Import/export of XML data possible

Secondary indexes

yes

All search fields are automatically indexed

yes

All properties auto-indexed by default

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL-like query language

Kusto Query Language (KQL), SQL subset

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

Java API
RESTful HTTP/JSON API

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

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

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

.Net
Groovy
Community Contributed Clients
Java
JavaScript
Perl
PHP
Python
Ruby

.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
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

JavaScript

Yes, possible languages: KQL, Python, R

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

by using the 'percolation' feature

JavaScript

yes

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

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

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

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Synchronous doc based replication. Get by ID may show delays up to 1 sec. Configurable write consistency: one, quorum, all

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

Multi-item ACID transactions with snapshot isolation within a partition

ACID

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.

Memcached and Redis integration

yes

User concepts

Access control

Access rights can be defined down to the item level

Azure Active Directory Authentication

Access rights for users and roles

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