DBMS > Google BigQuery vs. Kyligence Enterprise vs. Microsoft Azure AI Search vs. mSQL vs. OrigoDB

System Properties Comparison Google BigQuery vs. Kyligence Enterprise vs. Microsoft Azure AI Search vs. mSQL vs. OrigoDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed analytics engine for big data, built on top of Apache Kylin

Search-as-a-service for web and mobile app development

mSQL (Mini SQL) is a simple and lightweight RDBMS

A fully ACID in-memory object graph database

Primary database model

Relational DBMS

Search engine

Relational DBMS

Document store
Object oriented DBMS

Secondary database models

Vector DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Score	5.59
Rank	#63	Overall
	#7	Search engines

Score	1.27
Rank	#167	Overall
	#77	Relational DBMS

Score	0.00
Rank	#383	Overall
	#53	Document stores
	#20	Object oriented DBMS

Website

cloud.google.com/bigquery

kyligence.io/kyligence-enterprise

azure.microsoft.com/en-us/services/search

hughestech.com.au/products/msql

origodb.com

Technical documentation

cloud.google.com/bigquery/docs

learn.microsoft.com/en-us/azure/search

origodb.com/docs

Developer

Google

Kyligence, Inc.

Microsoft

Hughes Technologies

Robert Friberg et al

Initial release

2010

2016

2015

1994

2009

under the name LiveDB

Current release

4.4, October 2021

License

Commercial or Open Source

commercial

free licenses can be provided

Open Source

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

Server operating systems

hosted

Linux

hosted

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Linux
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

User defined using .NET types and collections

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.

can be achieved using .NET

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL for queries (using Apache Calcite)

A subset of ANSI SQL is implemented

no subqueries, aggregate functions, views, foreign keys, triggers

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

JDBC
ODBC

.NET Client API
HTTP API
LINQ

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
Java
JavaScript
Python

C
C++
Delphi
Java
Perl
PHP
Tcl

.Net

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

Triggers

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

none

horizontal partitioning

client side managed; servers are not synchronized

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Foreign keys

Referential integrity

depending on model

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Write ahead log

In-memory capabilities

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

yes

User concepts

Access control

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

using Azure authentication

Role based authorization

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