DBMS > Brytlyt vs. Fauna vs. PouchDB vs. Snowflake vs. Spark SQL

System Properties Comparison Brytlyt vs. Fauna vs. PouchDB vs. Snowflake vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Scalable GPU-accelerated RDBMS for very fast analytic and streaming workloads, leveraging PostgreSQL

Fauna provides a web-native interface, with support for GraphQL and custom business logic that integrates seamlessly with the rest of the serverless ecosystem. The underlying globally distributed storage and compute platform is fast, consistent, and reliable, with a modern security infrastructure.

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Document store

Relational DBMS

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

Score	0.29
Rank	#288	Overall
	#131	Relational DBMS

Score	1.52
Rank	#153	Overall
	#26	Document stores
	#14	Graph DBMS
	#71	Relational DBMS
	#13	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

docs.brytlyt.io

docs.fauna.com

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

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

Developer

Brytlyt

Fauna, Inc.

Apache Software Foundation

Snowflake Computing Inc.

Apache Software Foundation

Initial release

2016

2014

2012

2014

Current release

5.0, August 2023

7.1.1, June 2019

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

commercial

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++ and CUDA

Scala

JavaScript

Scala

Server operating systems

Linux
OS X
Windows

hosted

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

hosted

Linux
OS X
Windows

Data scheme

yes

schema-free

yes

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

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.

yes

specific XML-type available, but no XML query functionality.

yes

Secondary indexes

yes

via views

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
Perl
Python
Tcl

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

JavaScript

JavaScript (Node.js)
Python

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in PL/pgSQL

user defined functions

View functions in JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

consistent hashing

Sharding

with a proxy-based framework, named couchdb-lounge

yes

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

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

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

by using IndexedDB, WebSQL or LevelDB as backend

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

fine grained access rights according to SQL-standard

Identity management, authentication, and access control

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

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