DBMS > Google BigQuery vs. Microsoft Azure Data Explorer vs. OrigoDB vs. RDFox

System Properties Comparison Google BigQuery vs. Microsoft Azure Data Explorer vs. OrigoDB vs. RDFox

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Fully managed big data interactive analytics platform

A fully ACID in-memory object graph database

High performance knowledge graph and semantic reasoning engine

Primary database model

Relational DBMS

column oriented

Document store
Object oriented DBMS

Graph DBMS
RDF store

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

Relational DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#19	Object oriented DBMS

Score	0.29
Rank	#300	Overall
	#24	Graph DBMS
	#13	RDF stores

Website

cloud.google.com/bigquery

azure.microsoft.com/services/data-explorer

origodb.com

www.oxfordsemantic.tech

Technical documentation

cloud.google.com/bigquery/docs

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

origodb.com/docs

docs.oxfordsemantic.tech

Developer

Google

Microsoft

Robert Friberg et al

Oxford Semantic Technologies

Initial release

2010

2019

2009

under the name LiveDB

2017

Current release

cloud service with continuous releases

6.0, Septermber 2022

License

Commercial or Open Source

commercial

Open Source

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

C++

Server operating systems

hosted

Linux
Windows

Linux
macOS
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

RDF schemas

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

User defined using .NET types and collections

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

can be achieved using .NET

Secondary indexes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

Kusto Query Language (KQL), SQL subset

APIs and other access methods

RESTful HTTP/JSON API

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

.NET Client API
HTTP API
LINQ

RESTful HTTP API
SPARQL 1.1

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net

C
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Yes, possible languages: KQL, Python, R

yes

Triggers

yes

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

yes

Domain Events

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

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. Replication either local, cross-facility or geo-redundant.

Source-replica replication

replication via a shared file system

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
Immediate Consistency

Immediate Consistency in stand-alone mode, Eventual Consistency in replicated setups

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

yes

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)

Azure Active Directory Authentication

Role based authorization

Roles, resources, and access types

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