DBMS > AntDB vs. Firebase Realtime Database vs. Machbase Neo vs. Microsoft Azure Data Explorer vs. Snowflake

System Properties Comparison AntDB vs. Firebase Realtime Database vs. Machbase Neo vs. Microsoft Azure Data Explorer vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A scalable, multi-tenant, MPP-architectured RDBMS for OLTP and OLAP operations, highly compatible with Oracle

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

TimeSeries DBMS for AIoT and BigData

Fully managed big data interactive analytics platform

Cloud-based data warehousing service for structured and semi-structured data

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	0.23
Rank	#317	Overall
	#141	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	0.17
Rank	#337	Overall
	#30	Time Series DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

www.asiainfo.com/en_us/product_aisware_antdb_detail.html

firebase.google.com/products/realtime-database

machbase.com

azure.microsoft.com/services/data-explorer

www.snowflake.com

Technical documentation

firebase.google.com/docs/database

machbase.com/dbms

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

docs.snowflake.net/manuals/index.html

Developer

AsiaInfo Technologies Limited

Google

acquired by Google 2014

Machbase

Microsoft

Snowflake Computing Inc.

Initial release

2012

2013

2019

2014

Current release

V8.0, August 2023

cloud service with continuous releases

License

Commercial or Open Source

commercial

free test version 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.

Implementation language

Server operating systems

hosted

Linux
macOS
Windows

hosted

Data scheme

yes

schema-free

yes

Fixed schema with schema-less datatypes (dynamic)

yes

support of semi-structured data formats (JSON, XML, Avro)

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

SQL2016 compliant

SQL-like query language

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

JDBC
MySQL protocol compliant
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

gRPC
HTTP REST
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC

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

CLI Client
JDBC
ODBC

Supported programming languages

Java
JavaScript
Objective-C

C
C#
C++
Go
Java
JavaScript
PHP

via ODBC
Python
R

via ODBC
Scala

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

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

limited functionality with using 'rules'

Yes, possible languages: KQL, Python, R

user defined functions

Triggers

yes

Callbacks are triggered when data changes

yes

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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

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

yes

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

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

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.

yes

volatile and lookup table

User concepts

Access control

yes, based on authentication and database rules

simple password-based access control

Azure Active Directory Authentication

Users with fine-grained authorization concept, user roles and pluggable authentication

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