DBMS > Alibaba Cloud Table Store vs. Firebase Realtime Database vs. IBM Db2 vs. MariaDB vs. Microsoft Azure Data Explorer

System Properties Comparison Alibaba Cloud Table Store vs. Firebase Realtime Database vs. IBM Db2 vs. MariaDB vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

Description

A fully managed Wide Column Store for large quantities of semi-structured data with real-time access

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

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

MySQL application compatible open source RDBMS, enhanced with high availability, security, interoperability and performance capabilities. MariaDB ColumnStore provides a column-oriented storage engine and MariaDB Xpand supports distributed SQL.

Fully managed big data interactive analytics platform

Primary database model

Wide column store

Document store

Relational DBMS

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

Relational DBMS

column oriented

Secondary database models

Document store
RDF store

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

with Db2 Spatial Extender

Document store
Graph DBMS

with OQGraph storage engine
Spatial 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	0.26
Rank	#301	Overall
	#12	Wide column stores

Score	14.29
Rank	#39	Overall
	#6	Document stores

Score	128.46
Rank	#8	Overall
	#5	Relational DBMS

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

www.alibabacloud.com/product/table-store

firebase.google.com/products/realtime-database

www.ibm.com/products/db2

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

azure.microsoft.com/services/data-explorer

Technical documentation

www.alibabacloud.com/help/en/tablestore

firebase.google.com/docs/database

www.ibm.com/docs/en/db2

mariadb.com/kb/en/library

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

Developer

Alibaba

Google

acquired by Google 2014

IBM

MariaDB Corporation Ab (MariaDB Enterprise),
MariaDB Foundation (community MariaDB Server)

The lead developer Monty Widenius is the original author of MySQL

Microsoft

Initial release

2016

2012

1983

host version

2009

Fork of MySQL, which was first released in 1995

2019

Current release

12.1, October 2016

11.3.2, February 2024

cloud service with continuous releases

License

Commercial or Open Source

commercial

free version is available

Open Source

GPL version 2, commercial enterprise subscription available

commercial

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.

STACKIT MariaDB offers MariaDB in a fully managed version in enterprise grade, 100% GDPR-compliant.

Implementation language

C and C++

Server operating systems

hosted

AIX
HP-UX
Linux
Solaris
Windows
z/OS

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

hosted

Data scheme

schema-free

yes

Dynamic columns are supported

Fixed schema with schema-less datatypes (dynamic)

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

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

yes

with proprietary extensions

Kusto Query Language (KQL), SQL subset

APIs and other access methods

HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET
JDBC
JSON style queries

MongoDB compatible
ODBC
XQuery

ADO.NET
JDBC
ODBC
Proprietary native API

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

Supported programming languages

Java
Python

Java
JavaScript
Objective-C

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

Ada
C
C#
C++
D
Eiffel
Erlang
Go
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

limited functionality with using 'rules'

yes

PL/SQL compatibility added with version 10.3

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

Sharding

Implicit feature of the cloud service

Sharding

only with Windows/Unix/Linux Version

several options for horizontal partitioning and Sharding

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes

with separate tools (MQ, InfoSphere)

Multi-source replication
Source-replica replication

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

Immediate Consistency

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

Atomic single-row operations

yes

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

In-memory capabilities

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

yes

with MEMORY storage engine

User concepts

Access control

Access rights based on subaccounts and tokens

yes, based on authentication and database rules

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

Web, SaaS and Cloud operational applications that require high availability, scalability...
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Key customers

Deutsche Bank, DBS Bank, Nasdaq, Red Hat, ServiceNow, Verizon and Walgreens Featured...
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Market metrics

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Licensing and pricing models

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