DBMS > Datomic vs. Snowflake vs. Spark SQL vs. SQream DB

System Properties Comparison Datomic vs. Snowflake vs. Spark SQL vs. SQream DB

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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

Cloud-based data warehousing service for structured and semi-structured data

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

a GPU-based, columnar RDBMS for big data analytics workloads

Primary database model

Relational DBMS

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

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	0.70
Rank	#227	Overall
	#104	Relational DBMS

Website

Technical documentation

docs.datomic.com

docs.snowflake.net/manuals/index.html

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

docs.sqream.com

Developer

Cognitect

Snowflake Computing Inc.

Apache Software Foundation

SQream Technologies

Initial release

2012

2014

2017

Current release

1.0.6735, June 2023

3.5.0 ( 2.13), September 2023

2022.1.6, December 2022

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

Scala

C++, CUDA, Haskell, Java, Scala

Server operating systems

All OS with a Java VM

hosted

Linux
OS X
Windows

Linux

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

CLI Client
JDBC
ODBC

JDBC
ODBC

.Net
JDBC
ODBC

Supported programming languages

Clojure
Java

JavaScript (Node.js)
Python

Java
Python
R
Scala

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

user defined functions in Python

Triggers

By using transaction functions

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

But extensive use of caching in the application peers

yes

yes, utilizing Spark Core

horizontal and vertical partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

yes

none

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

yes

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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