DBMS > DataFS vs. Elasticsearch vs. Microsoft Azure Synapse Analytics

System Properties Comparison DataFS vs. Elasticsearch vs. Microsoft Azure Synapse Analytics

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Editorial information provided by DB-Engines

Name

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

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

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

Primary database model

Object oriented DBMS

Search engine

Relational DBMS

Secondary database models

Graph DBMS

Document store
Spatial DBMS
Vector DBMS

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

Score	0.06
Rank	#354	Overall
	#15	Object oriented DBMS

Score	135.35
Rank	#7	Overall
	#1	Search engines

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Website

newdatabase.com

www.elastic.co/elasticsearch

azure.microsoft.com/services/synapse-analytics

Technical documentation

dev.mobiland.com/Overview.xsp

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

docs.microsoft.com/azure/synapse-analytics

Developer

Mobiland AG

Elastic

Microsoft

Initial release

2018

2010

2016

Current release

1.1.263, October 2022

8.6, January 2023

License

Commercial or Open Source

commercial

Open Source

Elastic License

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

C++

Server operating systems

Windows

All OS with a Java VM

hosted

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

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

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

All search fields are automatically indexed

yes

SQL

Support of SQL

SQL-like query language

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

Java API
RESTful HTTP/JSON API

ADO.NET
JDBC
ODBC

Supported programming languages

.Net
C
C#
C++
VB.Net

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

C#
Java
PHP

Server-side scripts

Stored procedures

yes

Transact SQL

Triggers

no, except callback-events from server when changes happened

yes

by using the 'percolation' feature

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

Sharding, horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

ES-Hadoop Connector

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

Immediate Consistency

Foreign keys

Referential integrity

yes

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

Transaction concepts

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

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

User concepts

Access control

Windows-Profile

yes

More information provided by the system vendor

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More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2017
2 January 2018, Paul Andlinger, Matthias Gelbmann

Elasticsearch moved into the top 10 most popular database management systems
3 July 2017, Matthias Gelbmann

MySQL, PostgreSQL and Redis are the winners of the March ranking
2 March 2016, Paul Andlinger

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