DBMS > atoti vs. GridDB vs. Microsoft Azure Data Explorer vs. MySQL vs. Weaviate

System Properties Comparison atoti vs. GridDB vs. Microsoft Azure Data Explorer vs. MySQL vs. Weaviate

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.

Scalable in-memory time series database optimized for IoT and Big Data

Fully managed big data interactive analytics platform

Widely used open source RDBMS

An AI-native realtime vector database engine that integrates scalable machine learning models.

Primary database model

column oriented

Key/Value like access via memcached API

Vector DBMS

Secondary database models

Key-value store
Relational DBMS

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

Document store
Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	1083.74
Rank	#2	Overall
	#2	Relational DBMS

Score	1.73
Rank	#143	Overall
	#5	Vector DBMS

Website

atoti.io

griddb.net

azure.microsoft.com/services/data-explorer

www.mysql.com

github.com/weaviate/weaviate
weaviate.io

Technical documentation

docs.atoti.io

docs.griddb.net

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

dev.mysql.com/doc

weaviate.io/developers/weaviate

Developer

ActiveViam

Toshiba Corporation

Microsoft

Oracle

since 2010, originally MySQL AB, then Sun

Weaviate B.V.

Initial release

2013

2019

1995

2019

Current release

5.1, August 2022

cloud service with continuous releases

8.4.0, April 2024

1.19, May 2023

License

Commercial or Open Source

commercial

free versions available

Open Source

AGPL version 3 and Apache License, version 2.0 , commercial license (standard and advanced editions) also available

commercial

Open Source

GPL version 2. Commercial licenses with extended functionallity are available

Open Source

commercial license available with Weaviate Enterprise

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.

Aiven for MySQL: Fully managed MySQL, deployable in the cloud of your choice, with seamless integrations and lightning-fast setup.

Implementation language

Java

C++

C and C++

Server operating systems

Linux

hosted

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

yes, maps to GraphQL interface

Typing

predefined data types such as float or date

yes

numerical, string, blob, geometry, boolean, timestamp

yes

bool, datetime, dynamic, guid, int, long, real, string, timespan, double: docs.microsoft.com/en-us/azure/kusto/query/scalar-data-types

yes

string, int, float, geo point, date, cross reference, fuzzy references

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

yes

all fields are automatically indexed

yes

all data objects are indexed in a semantic vector space (the Contextionary), all primitive fields are indexed

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL92, SQL-like TQL (Toshiba Query Language)

Kusto Query Language (KQL), SQL subset

yes

with proprietary extensions

GraphQL is used as query language

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

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

ADO.NET
JDBC
ODBC
Proprietary native API

GraphQL query language
RESTful HTTP/JSON API

Supported programming languages

C
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

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

Ada
C
C#
C++
D
Delphi
Eiffel
Erlang
Haskell
Java
JavaScript (Node.js)
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

JavaScript / TypeScript
Python

Server-side scripts

Stored procedures

Python

Yes, possible languages: KQL, Python, R

yes

proprietary syntax

Triggers

yes

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

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

Implicit feature of the cloud service

horizontal partitioning, sharding with MySQL Cluster or MySQL Fabric

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

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

Multi-source replication
Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Connector for using GridDB as an input source and output destination for Hadoop MapReduce jobs

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate consistency within container, eventual consistency across containers

Eventual Consistency
Immediate Consistency

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

not for MyISAM storage engine

Transaction concepts

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

ACID at container level

ACID

not for MyISAM storage engine

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

table locks or row locks depending on storage engine

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

Access rights for users can be defined per database

Azure Active Directory Authentication

Users with fine-grained authorization concept

no user groups or roles

API Keys
OpenID Connect Discovery

More information provided by the system vendor

Specific characteristics

GridDB is a highly scalable, in-memory time series database optimized for IoT and...
» more

Weaviate is an open source vector database that is robust, scalable, cloud-native,...
» more

Competitive advantages

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
» more

Flexible deployment - Free, open source or fully-managed cloud vector database service...
» more

Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
» more

As a database supporting the development of generative AI and semantic search applications...
» more

Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
» more

All companies that have data.
» more

Market metrics

GitHub trending repository
» more

As of mid 2023: Over 2 million open source downloads 3500+ Weaviate Slack community...
» more

Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
» more

Weaviate is open-source, and free to use. Weaviate is also available as a fully managed...
» 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.