DBMS > GridDB vs. IBM Db2 Event Store vs. Microsoft Azure Data Explorer vs. SQLite vs. Stardog

System Properties Comparison GridDB vs. IBM Db2 Event Store vs. Microsoft Azure Data Explorer vs. SQLite vs. Stardog

Editorial information provided by DB-Engines

Name

Description

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

Distributed Event Store optimized for Internet of Things use cases

Fully managed big data interactive analytics platform

Widely used embeddable, in-process RDBMS

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Time Series DBMS

Event Store
Time Series DBMS

Relational DBMS

column oriented

Relational DBMS

Graph DBMS
RDF store

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	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	4.38
Rank	#77	Overall
	#41	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Score	2.02
Rank	#123	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

griddb.net

www.ibm.com/products/db2-event-store

azure.microsoft.com/services/data-explorer

www.sqlite.org

www.stardog.com

Technical documentation

docs.griddb.net

www.ibm.com/docs/en/db2-event-store

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

www.sqlite.org/docs.html

docs.stardog.com

Developer

Toshiba Corporation

IBM

Microsoft

Dwayne Richard Hipp

Stardog-Union

Initial release

2013

2017

2019

2000

2010

Current release

5.1, August 2022

2.0

cloud service with continuous releases

3.45.3 (15 April 2024), April 2024

7.3.0, May 2020

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

free developer edition available

commercial

Open Source

Public Domain

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

C and C++

Java

Server operating systems

Linux

Linux, macOS, Windows for the developer addition

hosted

server-less

Linux
macOS
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

dynamic column types

schema-free and OWL/RDFS-schema support

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

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

Import/export of XML data possible

Secondary indexes

yes

all fields are automatically indexed

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

SQL92, SQL-like TQL (Toshiba Query Language)

yes

through the embedded Spark runtime

Kusto Query Language (KQL), SQL subset

yes

SQL-92 is not fully supported

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC
Proprietary protocol
RESTful HTTP/JSON API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

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

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

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

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

.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

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

Yes, possible languages: KQL, Python, R

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

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

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Active-active shard replication

yes

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

none

Multi-source replication in HA-Cluster

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

Eventual Consistency
Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

Transaction concepts

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

ACID at container level

ACID

Concurrency

Support for concurrent manipulation of data

yes

No - written data is immutable

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

fine grained access rights according to SQL-standard

Azure Active Directory Authentication

Access rights for users and roles

More information provided by the system vendor

Specific characteristics

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

1. Optimized for IoT Equipped with Toshiba's proprietary key-container data model...
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Typical application scenarios

Factory IoT, Automative Industry, Energy, BEMS, Smart Community, Monitoring system.
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Key customers

Denso International [see use case ] An Electric Power company [see use case ] Ishinomaki...
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Market metrics

GitHub trending repository
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Licensing and pricing models

Open Source license (AGPL v3 & Apache v2) Commercial license (subscription)
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and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.