DBMS > Datomic vs. IBM Cloudant vs. Spark SQL

System Properties Comparison Datomic vs. IBM Cloudant vs. Spark SQL

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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

Database as a Service offering based on Apache CouchDB

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Relational DBMS

Document store

Relational DBMS

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

Score	1.76
Rank	#145	Overall
	#66	Relational DBMS

Score	2.73
Rank	#108	Overall
	#20	Document stores

Score	19.15
Rank	#33	Overall
	#20	Relational DBMS

Website

www.datomic.com

www.ibm.com/products/cloudant

spark.apache.org/sql

Technical documentation

docs.datomic.com

cloud.ibm.com/docs/Cloudant

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Cognitect

IBM, Apache Software Foundation

IBM acquired Cloudant in February 2014

Apache Software Foundation

Initial release

2012

2010

2014

Current release

1.0.6735, June 2023

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

limited edition free

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

Java, Clojure

Erlang

Scala

Server operating systems

All OS with a Java VM

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

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

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

Supported programming languages

Clojure
Java

C#
Java
JavaScript
Objective-C
PHP
Ruby

Java
Python
R
Scala

Server-side scripts

Stored procedures

yes

Transaction Functions

View functions (Map-Reduce) in JavaScript

Triggers

By using transaction functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

Multi-source replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

atomic operations within a document possible

Concurrency

Support for concurrent manipulation of data

yes

Optimistic locking

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

Access rights for users can be defined per database

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