DBMS > Heroic vs. Microsoft Azure Synapse Analytics vs. RDF4J vs. SpatiaLite

System Properties Comparison Heroic vs. Microsoft Azure Synapse Analytics vs. RDF4J vs. SpatiaLite

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Editorial information provided by DB-Engines

Name

Description

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

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

RDF4J is a Java framework for processing RDF data, supporting both memory-based and a disk-based storage.

Spatial extension of SQLite

Primary database model

Secondary database models

Relational DBMS

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

Score	0.51
Rank	#255	Overall
	#21	Time Series DBMS

Score	20.56
Rank	#31	Overall
	#19	Relational DBMS

Score	0.69
Rank	#230	Overall
	#9	RDF stores

Score	1.60
Rank	#149	Overall
	#3	Spatial DBMS

Website

github.com/spotify/heroic

azure.microsoft.com/services/synapse-analytics

rdf4j.org

www.gaia-gis.it/fossil/libspatialite/index

Technical documentation

spotify.github.io/heroic

docs.microsoft.com/azure/synapse-analytics

rdf4j.org/documentation

www.gaia-gis.it/gaia-sins/spatialite_topics.html

Developer

Spotify

Microsoft

Since 2016 officially forked into an Eclipse project, former developer was Aduna Software.

Alessandro Furieri

Initial release

2014

2016

2004

2008

Current release

5.0.0, August 2020

License

Commercial or Open Source

Open Source

Apache 2.0

commercial

Open Source

Eclipse Distribution License (EDL), v1.0.

Open Source

MPL 1.1, GPL v2.0 or LGPL v2.1

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++

Java

C++

Server operating systems

hosted

Linux
OS X
Unix
Windows

server-less

Data scheme

schema-free

yes

RDF Schemas

yes

Typing

predefined data types such as float or date

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.

Secondary indexes

yes

via Elasticsearch

yes

SQL

Support of SQL

yes

APIs and other access methods

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
ODBC

Java API
RIO

RDF Input/Output
Sail API
SeRQL

Sesame RDF Query Language
Sesame REST HTTP Protocol
SPARQL

Supported programming languages

C#
Java
PHP

Java
PHP
Python

Server-side scripts

Stored procedures

Transact SQL

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding, horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

Immediate Consistency

Foreign keys

Referential integrity

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

yes

Transaction concepts

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

ACID

Isolation support depends on the API used

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

in-memory storage is supported as well

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

yes

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More resources

DB-Engines blog posts

Spatial database management systems
6 April 2021, Matthias Gelbmann

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