DBMS > Apache Impala vs. Faircom EDGE vs. Quasardb vs. SQLite

System Properties Comparison Apache Impala vs. Faircom EDGE vs. Quasardb vs. SQLite

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Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for Hadoop

FairCom EDGE is an Industry 4.0 solution built to integrate, collect, aggregate and synchronize mission-critical data in edge computing environments

Distributed, high-performance timeseries database

Widely used embeddable, in-process RDBMS

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	0.09
Rank	#362	Overall
	#53	Key-value stores
	#155	Relational DBMS

Score	0.21
Rank	#322	Overall
	#29	Time Series DBMS

Score	111.41
Rank	#10	Overall
	#7	Relational DBMS

Website

impala.apache.org

www.faircom.com/products/faircom-edge

quasar.ai

www.sqlite.org

Technical documentation

impala.apache.org/impala-docs.html

docs.faircom.com/docs/en/UUID-23d4f1fd-d213-f6d5-b92e-9b7475baa14e.html

doc.quasar.ai/master

www.sqlite.org/docs.html

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

FairCom Corporation

quasardb

Dwayne Richard Hipp

Initial release

2013

1979

2009

2000

Current release

4.1.0, June 2022

V3, October 2020

3.14.1, January 2024

3.46.0 (23 May 2024), May 2024

License

Commercial or Open Source

Open Source

Apache Version 2

commercial

Restricted, free version available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Public Domain

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++

ANSI C, C++

C++

Server operating systems

Linux

Android
Linux

ARM, x86
Raspbian
Windows

BSD
Linux
OS X
Windows

server-less

Data scheme

yes

Flexible Schema (defined schema, partial schema, schema free)

schema-free

yes

dynamic column types

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

yes

integer and binary

yes

not rigid because of 'dynamic typing' concept.

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

with tags

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

ANSI SQL queries

SQL-like query language

yes

SQL-92 is not fully supported

APIs and other access methods

JDBC
ODBC

ADO.NET
Direct SQL
IoT Microservice layer
JDBC
MQTT (Message Queue Telemetry Transport)
ODBC
RESTful HTTP API

HTTP API

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

Supported programming languages

All languages supporting JDBC/ODBC

C
C#
C++
Java
JavaScript
PHP
Python
VB.Net

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

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

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

yes

.Net, JavaScript, C/C++

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

File partitioning

Customizable business rules for partitioning

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

yes

Synchronous and asynchronous realtime replication based on transaction logs

Source-replica replication with selectable replication factor

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

Immediate Consistency
Tunable Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

yes

via file-system locks

Durability

Support for making data persistent

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

Fine grained user, group and file access rights managed across SQL (per ANSI standard) and NoSQL.

Cryptographically strong user authentication and audit trail

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