DBMS > Blueflood vs. Hazelcast vs. MariaDB vs. Microsoft Access vs. Microsoft Azure Data Explorer

System Properties Comparison Blueflood vs. Hazelcast vs. MariaDB vs. Microsoft Access vs. Microsoft Azure Data Explorer

Editorial information provided by DB-Engines

Name

Description

Scalable TimeSeries DBMS based on Cassandra

A widely adopted in-memory data grid

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.

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

Fully managed big data interactive analytics platform

Primary database model

Time Series DBMS

Key-value store

Relational DBMS

column oriented

Secondary database models

Document store

JSON support with IMDG 3.12

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	0.06
Rank	#353	Overall
	#34	Time Series DBMS

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	93.21
Rank	#13	Overall
	#9	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Website

blueflood.io

hazelcast.com

mariadb.com

Site of MariaDB Corporation
mariadb.org

Site of MariaDB Foundation

www.microsoft.com/en-us/microsoft-365/access

azure.microsoft.com/services/data-explorer

Technical documentation

github.com/rax-maas/blueflood/wiki

hazelcast.org/imdg/docs

mariadb.com/kb/en/library

developer.microsoft.com/en-us/access

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

Developer

Rackspace

Hazelcast

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

The lead developer Monty Widenius is the original author of MySQL

Microsoft

Initial release

2013

2008

2009

Fork of MySQL, which was first released in 1995

1992

2019

Current release

5.3.6, November 2023

11.3.2, February 2024

1902 (16.0.11328.20222), March 2019

cloud service with continuous releases

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache Version 2; commercial licenses available

Open Source

GPL version 2, commercial enterprise subscription available

commercial

Bundled with Microsoft Office

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

Java

C and C++

C++

Server operating systems

Linux
OS X

All OS with a Java VM

FreeBSD
Linux
Solaris
Windows

ColumnStore storage engine not available on Windows

Windows

Not a real database server, but making use of DLLs

hosted

Data scheme

predefined scheme

schema-free

yes

Dynamic columns are supported

yes

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

SQL-like query language

yes

with proprietary extensions

yes

but not compliant to any SQL standard

Kusto Query Language (KQL), SQL subset

APIs and other access methods

HTTP REST

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
JDBC
ODBC
Proprietary native API

ADO.NET
DAO
ODBC
OLE DB

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

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

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

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

yes

PL/SQL compatibility added with version 10.3

yes

since Access 2010 using the ACE-engine

Yes, possible languages: KQL, Python, R

Triggers

yes

Events

yes

since Access 2010 using the ACE-engine

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

several options for horizontal partitioning and Sharding

none

Sharding

Implicit feature of the cloud service

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

yes

Replicated Map

Multi-source replication
Source-replica replication

none

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

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency

Eventual Consistency
Immediate Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

not for MyISAM storage engine

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

not for in-memory storage engine

yes

but no files for transaction logging

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

Role-based access control

fine grained access rights according to SQL-standard

a simple user-level security was built in till version Access 2003

Azure Active Directory Authentication

More information provided by the system vendor

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