DBMS > atoti vs. Datomic vs. Lovefield vs. Snowflake

System Properties Comparison atoti vs. Datomic vs. Lovefield vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Embeddable relational database for web apps written in pure JavaScript

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

Primary database model

Object oriented DBMS

Relational DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	1.66
Rank	#144	Overall
	#66	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

atoti.io

www.datomic.com

google.github.io/lovefield

www.snowflake.com

Technical documentation

docs.atoti.io

docs.datomic.com

github.com/google/lovefield/blob/master/docs/spec_index.md

docs.snowflake.net/manuals/index.html

Developer

ActiveViam

Cognitect

Google

Snowflake Computing Inc.

Initial release

2012

2014

Current release

1.0.7075, December 2023

2.1.12, February 2017

License

Commercial or Open Source

commercial

free versions available

commercial

limited edition free

Open Source

Apache 2.0

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

Java, Clojure

JavaScript

Server operating systems

All OS with a Java VM

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

hosted

Data scheme

yes

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

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.

yes

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

Clojure
Java

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Python

yes

Transaction Functions

user defined functions

Triggers

By using transaction functions

Using read-only observers

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

none

But extensive use of caching in the application peers

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

But extensive use of caching in the application peers

none

yes

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, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

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

yes, by using IndexedDB or the cloud service Firebase Realtime Database

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

yes

using MemoryDB

User concepts

Access control

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

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