DBMS > Microsoft Azure Data Explorer vs. TimesTen vs. Ultipa vs. Yanza

System Properties Comparison Microsoft Azure Data Explorer vs. TimesTen vs. Ultipa vs. Yanza

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Editorial information provided by DB-Engines

Name

Yanza seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

Description

Fully managed big data interactive analytics platform

In-Memory RDBMS compatible to Oracle

High performance Graph DBMS supporting HTAP high availability cluster deployment

Time Series DBMS for IoT Applications

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	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Score	0.19
Rank	#330	Overall
	#30	Graph DBMS

Website

azure.microsoft.com/services/data-explorer

www.oracle.com/database/technologies/related/timesten.html

www.ultipa.com

yanza.com

Technical documentation

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

docs.oracle.com/database/timesten-18.1

www.ultipa.com/document

Developer

Microsoft

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Ultipa

Yanza

Initial release

2019

1998

2019

2015

Current release

cloud service with continuous releases

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free version available

Cloud-based only

Only available as a cloud service

yes

but mainly used as a service provided by Yanza

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Server operating systems

hosted

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

Windows

Data scheme

Fixed schema with schema-less datatypes (dynamic)

yes

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

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

all fields are automatically indexed

yes

SQL

Support of SQL

Kusto Query Language (KQL), SQL subset

yes

APIs and other access methods

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

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

RESTful HTTP API

HTTP API

Supported programming languages

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

C
C++
Java
PL/SQL

C++
Go
Java
JavaScript (Node.js)
Python

any language that supports HTTP calls

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

PL/SQL

Triggers

yes

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

yes

Timer and event based

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

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.

Multi-source replication
Source-replica replication

none

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

Eventual Consistency
Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

by means of logfiles and checkpoints

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

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

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More resources

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