DBMS > Datomic vs. Microsoft Azure Synapse Analytics vs. SpaceTime

System Properties Comparison Datomic vs. Microsoft Azure Synapse Analytics vs. SpaceTime

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Editorial information provided by DB-Engines

Name

Description

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

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

SpaceTime is a spatio-temporal DBMS with a focus on performance.

Primary database model

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	22.71
Rank	#30	Overall
	#18	Relational DBMS

Score	0.00
Rank	#396	Overall
	#8	Spatial DBMS

Website

www.datomic.com

azure.microsoft.com/services/synapse-analytics

www.mireo.com/spacetime

Technical documentation

docs.datomic.com

docs.microsoft.com/azure/synapse-analytics

Developer

Cognitect

Microsoft

Mireo

Initial release

2012

2016

2020

Current release

1.0.6735, June 2023

License

Commercial or Open Source

commercial

limited edition free

commercial

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, Clojure

C++

Server operating systems

All OS with a Java VM

hosted

Linux

Data scheme

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

SQL

Support of SQL

yes

A subset of ANSI SQL is implemented

APIs and other access methods

RESTful HTTP API

ADO.NET
JDBC
ODBC

RESTful HTTP API

Supported programming languages

Clojure
Java

C#
Java
PHP

C#
C++
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

Transact SQL

Triggers

By using transaction functions

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding, horizontal partitioning

Fixed-grid hypercubes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

Real-time block device replication (DRBD)

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

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

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

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

User concepts

Access control

yes

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