DBMS > Apache Impala vs. Fauna vs. Firebase Realtime Database vs. Riak TS

System Properties Comparison Apache Impala vs. Fauna vs. Firebase Realtime Database vs. Riak TS

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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.

Cloud-hosted realtime document store. iOS, Android, and JavaScript clients share one Realtime Database instance and automatically receive updates with the newest data.

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Primary database model

Relational DBMS

Document store
Graph DBMS
Relational DBMS
Time Series DBMS

Document store

Time Series DBMS

Secondary database models

Document store

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

Score	10.63
Rank	#40	Overall
	#24	Relational DBMS

Score	1.55
Rank	#143	Overall
	#26	Document stores
	#13	Graph DBMS
	#66	Relational DBMS
	#13	Time Series DBMS

Score	13.60
Rank	#39	Overall
	#6	Document stores

Score	0.17
Rank	#318	Overall
	#27	Time Series DBMS

Website

impala.apache.org

fauna.com

firebase.google.com/products/realtime-database

Technical documentation

impala.apache.org/impala-docs.html

docs.fauna.com

firebase.google.com/docs/database

www.tiot.jp/riak-docs/riak/ts/latest

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Fauna, Inc.

Google

acquired by Google 2014

Open Source, formerly Basho Technologies

Initial release

2013

2014

2012

2015

Current release

4.1.0, June 2022

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

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

C++

Scala

Erlang

Server operating systems

Linux

hosted

Linux
OS X

Data scheme

yes

schema-free

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

restricted

SQL

Support of SQL

SQL-like DML and DDL statements

yes, limited

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

Android
iOS
JavaScript API
RESTful HTTP API

HTTP API
Native Erlang Interface

Supported programming languages

All languages supporting JDBC/ODBC

C#
Go
Java
JavaScript
Python
Ruby
Scala
Swift

Java
JavaScript
Objective-C

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

user defined functions

limited functionality with using 'rules'

Erlang

Triggers

Callbacks are triggered when data changes

yes

pre-commit hooks and post-commit hooks

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

consistent hashing

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Multi-source replication

selectable replication factor

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

if the client is offline
Immediate Consistency

if the client is online

Eventual Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

Transaction concepts

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

ACID

yes

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Identity management, authentication, and access control

yes, based on authentication and database rules

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More resources

DB-Engines blog posts

Cloud-based DBMS's popularity grows at high rates
12 December 2019, Paul Andlinger

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