DBMS > GridDB vs. Microsoft Azure Data Explorer vs. Newts

System Properties Comparison GridDB vs. Microsoft Azure Data Explorer vs. Newts

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Editorial information provided by DB-Engines

Name

Description

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

Fully managed big data interactive analytics platform

Time Series DBMS based on Cassandra

Primary database model

Time Series DBMS

Relational DBMS

column oriented

Time Series 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

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

Score	1.95
Rank	#128	Overall
	#10	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Website

griddb.net

azure.microsoft.com/services/data-explorer

opennms.github.io/newts

Technical documentation

docs.griddb.net

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

github.com/OpenNMS/newts/wiki

Developer

Toshiba Corporation

Microsoft

OpenNMS Group

Initial release

2013

2019

2014

Current release

5.1, August 2022

cloud service with continuous releases

License

Commercial or Open Source

Open Source

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

commercial

Open Source

Apache 2.0

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

C++

Java

Server operating systems

Linux

hosted

Linux
OS X
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

schema-free

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

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

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

Kusto Query Language (KQL), SQL subset

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

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

HTTP REST
Java API

Supported programming languages

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

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

Java

Server-side scripts

Stored procedures

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

Implicit feature of the cloud service

Sharding

based on Cassandra

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.

selectable replication factor

based on Cassandra

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

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

Transaction concepts

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

ACID at container level

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

Access rights for users can be defined per database

Azure Active Directory Authentication

More information provided by the system vendor

Specific characteristics

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

Competitive advantages

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

Typical application scenarios

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

Key customers

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

Market metrics

GitHub trending repository
» more

Licensing and pricing models

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

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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

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More resources

Recent citations in the news

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16 January 2024, global.toshiba

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1 November 2020, global.toshiba

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19 August 2022, global.toshiba

Toshiba's Distributed Database GridDB(R) Now Features Scale-Out and Scale-Up combo for Petabyte-scale Data ...
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