DBMS > Apache Impala vs. Fauna vs. IBM Db2 Event Store vs. PouchDB vs. Snowflake

System Properties Comparison Apache Impala vs. Fauna vs. IBM Db2 Event Store vs. PouchDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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.

Distributed Event Store optimized for Internet of Things use cases

JavaScript DBMS with an API inspired by CouchDB

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

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Event Store
Time Series DBMS

Document store

Relational DBMS

Secondary database models

Document store

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

Score	13.77
Rank	#40	Overall
	#24	Relational DBMS

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

Score	0.19
Rank	#323	Overall
	#2	Event Stores
	#28	Time Series DBMS

Score	2.28
Rank	#115	Overall
	#21	Document stores

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

impala.apache.org

fauna.com

www.ibm.com/products/db2-event-store

pouchdb.com

www.snowflake.com

Technical documentation

impala.apache.org/impala-docs.html

docs.fauna.com

www.ibm.com/docs/en/db2-event-store

pouchdb.com/guides

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Fauna, Inc.

IBM

Apache Software Foundation

Snowflake Computing Inc.

Initial release

2013

2014

2017

2012

2014

Current release

4.1.0, June 2022

2.0

7.1.1, June 2019

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

free developer edition available

Open Source

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

C++

Scala

C and C++

JavaScript

Server operating systems

Linux

hosted

Linux

Linux, macOS, Windows for the developer addition

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

hosted

Data scheme

yes

schema-free

yes

schema-free

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

via views

SQL

Support of SQL

SQL-like DML and DDL statements

yes

through the embedded Spark runtime

yes

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

ADO.NET
DB2 Connect
JDBC
ODBC
RESTful HTTP API

HTTP REST

only for PouchDB Server
JavaScript API

CLI Client
JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

C
C#
C++
Cobol
Delphi
Fortran
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby
Scala
Visual Basic

JavaScript

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

yes

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

Sharding

horizontal partitioning

consistent hashing

Sharding

with a proxy-based framework, named couchdb-lounge

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

Active-active shard replication

Multi-source replication

also with CouchDB databases
Source-replica replication

also with CouchDB databases

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

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

No - written data is immutable

yes

Durability

Support for making data persistent

yes

Yes - Synchronous writes to local disk combined with replication and asynchronous writes in parquet format to permanent shared storage

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

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Identity management, authentication, and access control

fine grained access rights according to SQL-standard

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

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