DBMS > Apache Impala vs. Firebase Realtime Database vs. SQLite vs. Trino

System Properties Comparison Apache Impala vs. Firebase Realtime Database vs. SQLite vs. Trino

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

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

Widely used embeddable, in-process RDBMS

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Relational DBMS

Document store

Relational DBMS

Secondary database models

Document store

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	13.64
Rank	#39	Overall
	#6	Document stores

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Score	4.99
Rank	#65	Overall
	#36	Relational DBMS

Website

impala.apache.org

firebase.google.com/products/realtime-database

www.sqlite.org

trino.io

Technical documentation

impala.apache.org/impala-docs.html

firebase.google.com/docs/database

www.sqlite.org/docs.html

trino.io/broadcast
trino.io/docs/current

Social network pages

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

Google

acquired by Google 2014

Dwayne Richard Hipp

Trino Software Foundation

Initial release

2013

2012

2000

2012

2020 rebranded from PrestoSQL

Current release

4.1.0, June 2022

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Open Source

Public Domain

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Starburst Galaxy offers a feature-rich user interface to connect all your data sources, manage your Trino clusters, and query your data.

Implementation language

C++

Java

Server operating systems

Linux

hosted

server-less

Linux
macOS

for devlopment

Data scheme

yes

schema-free

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

depending on connected data-source

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
ODBC

Android
iOS
JavaScript API
RESTful HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

All languages supporting JDBC/ODBC

Java
JavaScript
Objective-C

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

limited functionality with using 'rules'

yes, depending on connected data-source

Triggers

Callbacks are triggered when data changes

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

none

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

if the client is offline
Immediate Consistency

if the client is online

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

depending on connected data-source

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

yes, based on authentication and database rules

SQL standard access control

More information provided by the system vendor

Specific characteristics

Trino is the fastest open source, massively parallel processing SQL query engine...
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Competitive advantages

High performance analtyics and data processing of very large data sets Powerful ANSI...
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Typical application scenarios

Performant analytics query engine for data warehouses, data lakes, and data lakehouses...
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Key customers

Trino is widely adopted across the globe as freely-available open source software....
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Market metrics

33000+ commits in GitHub 8200+ stargazers in GitHub 1200+ pull requests merged in...
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Licensing and pricing models

Trino is an open source project and usage is therefore free. Commercial offerings...
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