DBMS > Citus vs. Microsoft Azure Data Explorer vs. Sequoiadb

System Properties Comparison Citus vs. Microsoft Azure Data Explorer vs. Sequoiadb

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Editorial information provided by DB-Engines

Name

Description

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Fully managed big data interactive analytics platform

NewSQL database with distributed OLTP and SQL

Primary database model

Relational DBMS

column oriented

Document store
Relational DBMS

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)
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	1.72
Rank	#133	Overall
	#65	Relational DBMS

Score	7.08
Rank	#61	Overall
	#34	Relational DBMS

Score	0.38
Rank	#261	Overall
	#39	Document stores
	#121	Relational DBMS

Website

www.citusdata.com

azure.microsoft.com/services/data-explorer

www.sequoiadb.com

Technical documentation

docs.citusdata.com

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

www.sequoiadb.com/en/index.php?m=Files&a=index

Developer

Microsoft

Sequoiadb Ltd.

Initial release

2010

2019

2013

Current release

8.1, December 2018

cloud service with continuous releases

License

Commercial or Open Source

Open Source

AGPL, commercial license also available

commercial

Open Source

Server: AGPL; Client: Apache V2

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

Linux

hosted

Linux

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

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

oid, date, timestamp, binary, regex

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

all fields are automatically indexed

yes

SQL

Support of SQL

yes

standard, with numerous extensions

Kusto Query Language (KQL), SQL subset

SQL-like query language

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

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

proprietary protocol using JSON

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

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

.Net
C++
Java
PHP
Python

Server-side scripts

Stored procedures

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

Yes, possible languages: KQL, Python, R

JavaScript

Triggers

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

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

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

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Document is locked during a transaction

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

simple password-based access control

More information provided by the system vendor

Specific characteristics

Azure Data Explorer is a fast and highly scalable data exploration service for log...
» more

Competitive advantages

Kusto Query Language (innovative query language, optimized for high performance data...
» more

Typical application scenarios

IoT applications IoT devices generate billions of sensor readings. Normalizing and...
» more

Key customers

Microsoft, DocuSign, Taboola, Bosch, Siemens Healthineers, Bühler, Ecolab, Zoomd
» more

Market metrics

Azure Data Explorer is the data service for Azure Monitor, Azure Time Series Insights,...
» more

Licensing and pricing models

An Azure Data Explorer cluster is a pair of engine and data management clusters which...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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