DBMS > Firebase Realtime Database vs. GBase vs. Microsoft Azure Data Explorer vs. SiteWhere

System Properties Comparison Firebase Realtime Database vs. GBase vs. Microsoft Azure Data Explorer vs. SiteWhere

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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.

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

Fully managed big data interactive analytics platform

M2M integration platform for persisting/querying time series data

Primary database model

Document store

Relational DBMS

column oriented

Time Series DBMS

Secondary database models

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	14.29
Rank	#39	Overall
	#6	Document stores

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

firebase.google.com/products/realtime-database

www.gbase.cn

azure.microsoft.com/services/data-explorer

github.com/sitewhere/sitewhere

Technical documentation

firebase.google.com/docs/database

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

sitewhere1.sitewhere.io/index.html

Developer

Google

acquired by Google 2014

General Data Technology Co., Ltd.

Microsoft

SiteWhere

Initial release

2012

2004

2019

2010

Current release

GBase 8a, GBase 8s, GBase 8c

cloud service with continuous releases

License

Commercial or Open Source

commercial

Open Source

Common Public Attribution License Version 1.0

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, Python

Java

Server operating systems

hosted

Linux

hosted

Linux
OS X
Windows

Data scheme

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

predefined scheme

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

SQL

Support of SQL

Standard with numerous extensions

Kusto Query Language (KQL), SQL subset

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
C API
JDBC
ODBC

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

HTTP REST

Supported programming languages

Java
JavaScript
Objective-C

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

user defined functions

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

horizontal partitioning (by range, list and hash) and vertical partitioning

Sharding

Implicit feature of the cloud service

Sharding

based on HBase

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

selectable replication factor

based on HBase

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

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.

User concepts

Access control

yes, based on authentication and database rules

yes

Azure Active Directory Authentication

Users with fine-grained authorization concept

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Related products and services

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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