DBMS > Microsoft Azure Data Explorer vs. SQLite vs. Trafodion vs. Yaacomo

System Properties Comparison Microsoft Azure Data Explorer vs. SQLite vs. Trafodion vs. Yaacomo

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Editorial information provided by DB-Engines

Name

Apache Trafodion has been retired in 2021. Therefore it is excluded from the DB-Engines Ranking.

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

Description

Fully managed big data interactive analytics platform

Widely used embeddable, in-process RDBMS

Transactional SQL-on-Hadoop DBMS

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Relational DBMS

column oriented

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)
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	3.28
Rank	#83	Overall
	#45	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Website

azure.microsoft.com/services/data-explorer

www.sqlite.org

trafodion.apache.org

yaacomo.com

Technical documentation

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

www.sqlite.org/docs.html

trafodion.apache.org/documentation.html

Developer

Microsoft

Dwayne Richard Hipp

Apache Software Foundation, originally developed by HP

Q2WEB GmbH

Initial release

2019

2000

2014

2009

Current release

cloud service with continuous releases

3.46.1 (13 August 2024), August 2024

2.3.0, February 2019

License

Commercial or Open Source

commercial

Open Source

Public Domain

Open Source

Apache 2.0

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++, Java

Server operating systems

hosted

server-less

Linux

Android
Linux
Windows

Data scheme

Fixed schema with schema-less datatypes (dynamic)

yes

dynamic column types

yes

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

not rigid because of 'dynamic typing' concept.

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

SQL-92 is not fully supported

yes

APIs and other access methods

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
ODBC

JDBC
ODBC

Supported programming languages

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

All languages supporting JDBC/ODBC/ADO.Net

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

Java Stored Procedures

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Sharding

horizontal partitioning

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.

none

yes, via HBase

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Spark connector (open source): github.com/Azure/azure-kusto-spark

yes

via user defined functions and HBase

Consistency concepts

Methods to ensure consistency in a distributed system

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

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

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

User concepts

Access control

Azure Active Directory Authentication

fine grained access rights according to SQL-standard

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