DBMS > Microsoft Azure Data Explorer vs. ObjectBox vs. OpenMLDB vs. RocksDB vs. Yanza

System Properties Comparison Microsoft Azure Data Explorer vs. ObjectBox vs. OpenMLDB vs. RocksDB vs. Yanza

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

Extremely fast embedded database for small devices, IoT and Mobile

An open-source machine learning database that provides a feature platform for training and inference

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

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

Time Series DBMS

Relational DBMS

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

Score	3.80
Rank	#81	Overall
	#43	Relational DBMS

Score	1.29
Rank	#166	Overall
	#5	Object oriented DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Website

azure.microsoft.com/services/data-explorer

objectbox.io

openmldb.ai

rocksdb.org

yanza.com

Technical documentation

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

docs.objectbox.io

openmldb.ai/docs/zh/main

github.com/facebook/rocksdb/wiki

Developer

Microsoft

ObjectBox Limited

4 Paradigm Inc.

Facebook, Inc.

Yanza

Initial release

2019

2017

2020

2013

2015

Current release

cloud service with continuous releases

2024-2 February 2024

9.2.1, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

Open Source

BSD

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.

Implementation language

C and C++

C++, Java, Scala

C++

Server operating systems

hosted

Android
iOS
Linux
macOS
Windows

Linux

Windows

Data scheme

Fixed schema with schema-less datatypes (dynamic)

yes

Fixed schema

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

Proprietary native API

JDBC
SQLAlchemy

C++ API
Java API

HTTP API

Supported programming languages

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

C
C++
Dart
Go
Java
JavaScript

planned (as of Jan 2019)
Kotlin
Python

planned (as of Jan 2019)
Swift

C++
Go
Java
Python
Scala

C
C++
Go
Java
Perl
Python
Ruby

any language that supports HTTP calls

Server-side scripts

Stored procedures

Yes, possible languages: KQL, Python, R

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

horizontal partitioning

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.

online/offline synchronization between client and server

Source-replica replication

yes

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

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

yes

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.

yes

User concepts

Access control

Azure Active Directory Authentication

yes

fine grained access rights according to SQL-standard

More information provided by the system vendor

News

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29 May 2024

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29 May 2024

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28 May 2024

Evolution of search: traditional vs vector search
23 May 2024

On-device Vector Database for Dart/Flutter
21 May 2024

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