DBMS > EsgynDB vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Netezza vs. RDF4J

System Properties Comparison EsgynDB vs. IBM Db2 vs. Microsoft Azure Data Explorer vs. Netezza vs. RDF4J

Editorial information provided by DB-Engines

Name

Description

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

Common in IBM host environments, 2 different versions for host and Windows/Linux

Fully managed big data interactive analytics platform

Data warehouse and analytics appliance part of IBM PureSystems

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Primary database model

Relational DBMS

Since Version 10.5 support for JSON/BSON documents compatible with MongoDB

Relational DBMS

column oriented

Relational DBMS

RDF store

Secondary database models

Document store
RDF store

in Db2 LUW (Linux, Unix, Windows)
Spatial DBMS

with Db2 Spatial Extender

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	0.15
Rank	#325	Overall
	#144	Relational DBMS

Score	123.05
Rank	#9	Overall
	#6	Relational DBMS

Score	3.28
Rank	#83	Overall
	#45	Relational DBMS

Score	7.56
Rank	#48	Overall
	#31	Relational DBMS

Score	0.72
Rank	#222	Overall
	#9	RDF stores

Website

www.esgyn.cn

www.ibm.com/products/db2

azure.microsoft.com/services/data-explorer

www.ibm.com/products/netezza

rdf4j.org

Technical documentation

www.ibm.com/docs/en/db2

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

rdf4j.org/documentation

Developer

Esgyn

IBM

Microsoft

IBM

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Initial release

2015

1983

host version

2019

2000

2004

Current release

12.1, October 2016

cloud service with continuous releases

License

Commercial or Open Source

commercial

free version is available

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

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

C and C++

Java

Server operating systems

Linux

AIX
HP-UX
Linux
Solaris
Windows
z/OS

hosted

Linux

included in appliance

Linux
OS X
Unix
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

RDF Schemas

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

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

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

JDBC
ODBC
OLE DB

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

All languages supporting JDBC/ODBC/ADO.Net

C
C#
C++
Cobol
Delphi
Fortran
Java
Perl
PHP
Python
Ruby
Visual Basic

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

C
C++
Fortran
Java
Lua
Perl
Python
R

Java
PHP
Python

Server-side scripts

Stored procedures

Java Stored Procedures

yes

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

only with Windows/Unix/Linux Version

Sharding

Implicit feature of the cloud service

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication between multi datacenters

yes

with separate tools (MQ, InfoSphere)

yes

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

Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

Users with fine-grained authorization concept

More information provided by the system vendor

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