DBMS > Firebase Realtime Database vs. KairosDB vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB

System Properties Comparison Firebase Realtime Database vs. KairosDB vs. Microsoft Azure Data Explorer vs. Percona Server for MongoDB

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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.

Distributed Time Series DBMS based on Cassandra or H2

Fully managed big data interactive analytics platform

A drop-in replacement for MongoDB Community Edition with enterprise-grade features.

Primary database model

column oriented

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	13.60
Rank	#39	Overall
	#6	Document stores

Score	0.60
Rank	#238	Overall
	#20	Time Series DBMS

Score	3.28
Rank	#83	Overall
	#45	Relational DBMS

Score	0.50
Rank	#250	Overall
	#39	Document stores

Website

firebase.google.com/products/realtime-database

github.com/kairosdb/kairosdb

azure.microsoft.com/services/data-explorer

www.percona.com/mongodb/software/percona-server-for-mongodb

Technical documentation

firebase.google.com/docs/database

kairosdb.github.io

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

docs.percona.com/percona-distribution-for-mongodb

Developer

Google

acquired by Google 2014

Microsoft

Percona

Initial release

2012

2013

2019

2015

Current release

1.2.2, November 2018

cloud service with continuous releases

3.4.10-2.10, November 2017

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

GPL Version 2

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

Linux
OS X
Windows

hosted

Linux

Data scheme

schema-free

Fixed schema with schema-less datatypes (dynamic)

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

Kusto Query Language (KQL), SQL subset

APIs and other access methods

Android
iOS
JavaScript API
RESTful HTTP API

Graphite protocol
HTTP REST
Telnet API

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

proprietary protocol using JSON

Supported programming languages

Java
JavaScript
Objective-C

Java
JavaScript

Node.js
PHP
Python

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

Actionscript
C
C#
C++
Clojure
ColdFusion
D
Dart
Delphi
Erlang
Go
Groovy
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Perl
PHP
PowerShell
Prolog
Python
R
Ruby
Scala
Smalltalk

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Yes, possible languages: KQL, Python, R

JavaScript

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

Sharding

based on Cassandra

Sharding

Implicit feature of the cloud service

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

yes

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.

yes

via In-Memory Engine

User concepts

Access control

yes, based on authentication and database rules

simple password-based access control

Azure Active Directory Authentication

Access rights for users and roles

More information provided by the system vendor

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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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