DBMS > GridGain vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Stardog

System Properties Comparison GridGain vs. Microsoft Azure Data Explorer vs. Microsoft Azure Synapse Analytics vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

GridGain is an in-memory computing platform, built on Apache Ignite

Fully managed big data interactive analytics platform

Elastic, large scale data warehouse service leveraging the broad eco-system of SQL Server

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

Primary database model

Columnar
Key-value store
Object oriented DBMS
Relational DBMS

Relational DBMS

column oriented

Relational DBMS

Graph DBMS
RDF 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

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

Score	1.48
Rank	#150	Overall
	#1	Columnar
	#26	Key-value stores
	#2	Object oriented DBMS
	#69	Relational DBMS

Score	3.28
Rank	#83	Overall
	#45	Relational DBMS

Score	18.98
Rank	#31	Overall
	#19	Relational DBMS

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

www.gridgain.com

azure.microsoft.com/services/data-explorer

azure.microsoft.com/services/synapse-analytics

www.stardog.com

Technical documentation

www.gridgain.com/docs/index.html

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

docs.microsoft.com/azure/synapse-analytics

docs.stardog.com

Developer

GridGain Systems, Inc.

Microsoft

Stardog-Union

Initial release

2007

2019

2016

2010

Current release

GridGain 8.5.1

cloud service with continuous releases

7.3.0, May 2020

License

Commercial or Open Source

commercial, open source

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

Java, C++, .Net, Python, REST, SQL

C++

Java

Server operating systems

Linux
OS X
Solaris
Windows
z/OS

hosted

Linux
macOS
Windows

Data scheme

yes

Fixed schema with schema-less datatypes (dynamic)

yes

schema-free and OWL/RDFS-schema support

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

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

ANSI-99 for query and DML statements, subset of DDL

Kusto Query Language (KQL), SQL subset

yes

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

APIs and other access methods

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

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

ADO.NET
JDBC
ODBC

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++
Java
PHP
Python
Ruby
Scala

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

C#
Java
PHP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes (compute grid and cache interceptors can be used instead)

Yes, possible languages: KQL, Python, R

Transact SQL

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes (cache interceptors and events)

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

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

yes

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

yes

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-table-constraints

yes

relationships in graphs

Transaction concepts

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

ACID

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

Role-based access control
Security Hooks for custom implementations

Azure Active Directory Authentication

yes

Access rights for users and roles

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