DBMS > atoti vs. Microsoft Azure Data Explorer vs. YottaDB

System Properties Comparison atoti vs. Microsoft Azure Data Explorer vs. YottaDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Fully managed big data interactive analytics platform

A fast and solid embedded Key-value store

Primary database model

Object oriented DBMS

Relational DBMS

column oriented

Key-value 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

using the Octo plugin

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

Score	0.49
Rank	#244	Overall
	#11	Object oriented DBMS

Score	2.94
Rank	#87	Overall
	#48	Relational DBMS

Score	0.19
Rank	#312	Overall
	#47	Key-value stores

Website

atoti.io

azure.microsoft.com/services/data-explorer

yottadb.com

Technical documentation

docs.atoti.io

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

yottadb.com/resources/documentation

Developer

ActiveViam

Microsoft

YottaDB, LLC

Initial release

2019

2001

Current release

cloud service with continuous releases

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

AGPL 3.0

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

Docker
Linux

Data scheme

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

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

SQL

Support of SQL

Multidimensional Expressions (MDX)

Kusto Query Language (KQL), SQL subset

by using the Octo plugin

APIs and other access methods

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

PostgreSQL wire protocol

using the Octo plugin
Proprietary protocol

Supported programming languages

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

C
Go
JavaScript (Node.js)
Lua
M
Perl
Python
Rust

Server-side scripts

Stored procedures

Python

Yes, possible languages: KQL, Python, R

Triggers

yes

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

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

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.

yes

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

Foreign keys

Referential integrity

Transaction concepts

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

optimistic locking

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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

Users and groups based on OS-security mechanisms

More information provided by the system vendor

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.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

Recent citations in the news

Best use of cloud: ActiveViam
28 November 2023, Risk.net

ActiveViam Announces Leadership Succession
4 September 2024, Business Wire

provided by Google News

Universal Destinations & Experiences proactively improves guest satisfaction with Azure Data Explorer
14 February 2025, Microsoft

Log and Telemetry Analytics Performance Benchmark
16 August 2022, Gigaom

Azure Data Explorer: Log and telemetry analytics benchmark
16 August 2022, azure.microsoft.com

Microsoft announces general availability of Azure Data Explorer and Azure Data Lake Storage Gen2
7 February 2019, VentureBeat

Azure Data Explorer Supports Native Ingestion from Amazon S3
7 September 2022, InfoQ.com

provided by Google News

Share this page