DBMS > Faircom EDGE vs. InfluxDB vs. Lovefield vs. SQLite vs. Teradata Aster

System Properties Comparison Faircom EDGE vs. InfluxDB vs. Lovefield vs. SQLite vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

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

DBMS for storing time series, events and metrics

Embeddable relational database for web apps written in pure JavaScript

Widely used embeddable, in-process RDBMS

Platform for big data analytics on multistructured data sources and types

Primary database model

Key-value store
Relational DBMS

Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

with GEO package

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

Score	0.02
Rank	#368	Overall
	#54	Key-value stores
	#156	Relational DBMS

Score	25.83
Rank	#28	Overall
	#1	Time Series DBMS

Score	0.29
Rank	#293	Overall
	#133	Relational DBMS

Score	114.32
Rank	#10	Overall
	#7	Relational DBMS

Website

www.faircom.com/products/faircom-edge

www.influxdata.com/products/influxdb-overview

google.github.io/lovefield

www.sqlite.org

Technical documentation

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

docs.influxdata.com/influxdb

github.com/google/lovefield/blob/master/docs/spec_index.md

www.sqlite.org/docs.html

Developer

FairCom Corporation

Google

Dwayne Richard Hipp

Teradata

Initial release

1979

2013

2014

2000

2005

Current release

V3, October 2020

2.7.6, April 2024

2.1.12, February 2017

3.45.3 (15 April 2024), April 2024

License

Commercial or Open Source

commercial

Restricted, free version available

Open Source

MIT-License; commercial enterprise version available

Open Source

Apache 2.0

Open Source

Public Domain

commercial

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

ANSI C, C++

JavaScript

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

Linux
OS X

through Homebrew

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

tested with Chrome, Firefox, IE, Safari

server-less

Linux

Data scheme

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

schema-free

yes

dynamic column types

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

defined schema within the relational store; partial schema or schema free in the Aster File Store

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

Numeric data and Strings

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.

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

SQL-like query language

via JavaScript builder pattern

yes

SQL-92 is not fully supported

yes

APIs and other access methods

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

HTTP API
JSON over UDP

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

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

.Net
Clojure
Erlang
Go
Haskell
Java
JavaScript
JavaScript (Node.js)
Lisp
Perl
PHP
Python
R
Ruby
Rust
Scala

JavaScript

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

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

R packages

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

Sharding

in enterprise version only

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

in enterprise version only

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency depending on configuration

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

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

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

yes

Depending on used storage engine

yes

using MemoryDB

yes

User concepts

Access control

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

simple rights management via user accounts

fine grained access rights according to SQL-standard

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