DBMS > EsgynDB vs. H2GIS vs. Microsoft Azure Data Explorer vs. NuoDB vs. Sadas Engine

System Properties Comparison EsgynDB vs. H2GIS vs. Microsoft Azure Data Explorer vs. NuoDB vs. Sadas Engine

Editorial information provided by DB-Engines

Name

Description

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Spatial extension of H2

Fully managed big data interactive analytics platform

NuoDB is a webscale distributed database that supports SQL and ACID transactions

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Primary database model

column oriented

Secondary database models

Relational 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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.89
Rank	#198	Overall
	#92	Relational DBMS

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Website

www.esgyn.cn

www.h2gis.org

azure.microsoft.com/services/data-explorer

www.3ds.com/nuodb-distributed-sql-database

www.sadasengine.com

Technical documentation

www.h2gis.org/docs/home

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

doc.nuodb.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

Developer

Esgyn

CNRS

Microsoft

Dassault Systèmes

originally NuoDB, Inc.

SADAS s.r.l.

Initial release

2015

2013

2019

2013

2006

Current release

cloud service with continuous releases

8.0

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

commercial

limited edition free

commercial

free trial version available

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

Java

C++

Server operating systems

Linux

hosted

Amazon EC2, Windows Azure, SoftLayer
Linux
OS X
Windows

AIX
Linux
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

Typing

predefined data types such as float or date

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

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

ADO.NET
JDBC
ODBC

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

ADO.NET
JDBC
ODBC

JDBC
ODBC
Proprietary protocol

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

Java

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

.Net
C
C++
Go
Java
JavaScript
JavaScript (Node.js)
PHP
Python
Ruby

.Net
C
C#
C++
Groovy
Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

based on H2

Yes, possible languages: KQL, Python, R

Java, SQL

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Implicit feature of the cloud service

data is dynamically stored/cached on the nodes where it is read/written

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

based on H2

yes

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

yes

Managed transparently by NuoDB

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

tunable commit protocol

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

yes

MVCC

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

Temporary table

yes

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

yes

based on H2

Azure Active Directory Authentication

Standard SQL roles/ privileges, Administrative Users

Access rights for users, groups and roles according to SQL-standard

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More resources

DB-Engines blog posts

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