DBMS > Firebase Realtime Database vs. H2GIS vs. Microsoft Azure Data Explorer vs. Oracle Rdb vs. SWC-DB

System Properties Comparison Firebase Realtime Database vs. H2GIS vs. Microsoft Azure Data Explorer vs. Oracle Rdb vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Spatial extension of H2

Fully managed big data interactive analytics platform

A high performance, scalable Wide Column DBMS

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

Time Series DBMS

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

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	0.00
Rank	#383	Overall
	#7	Spatial DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

firebase.google.com/products/realtime-database

www.h2gis.org

azure.microsoft.com/services/data-explorer

www.oracle.com/database/technologies/related/rdb.html

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

firebase.google.com/docs/database

www.h2gis.org/docs/home

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

www.oracle.com/database/technologies/related/rdb-doc.html

Developer

Google

acquired by Google 2014

CNRS

Microsoft

Oracle, originally developed by Digital Equipment Corporation (DEC)

Alex Kashirin

Initial release

2012

2013

2019

1984

2020

Current release

cloud service with continuous releases

7.4.1.1, 2021

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

LGPL 3.0

commercial

Open Source

GPL V3

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

hosted

HP Open VMS

Linux

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

Flexible Schema (defined schema, partial schema, schema free)

schema-free

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

SQL-like query language

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

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

Proprietary protocol
Thrift

Supported programming languages

Java
JavaScript
Objective-C

Java

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

C++

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

based on H2

Yes, possible languages: KQL, Python, R

Triggers

Callbacks are triggered when data changes

yes

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

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on H2

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

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

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

yes

ACID

yes, on a single node

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (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

User concepts

Access control

yes, based on authentication and database rules

yes

based on H2

Azure Active Directory Authentication

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More resources

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