DBMS > Faircom EDGE vs. Google BigQuery vs. Hypertable vs. IRONdb

System Properties Comparison Faircom EDGE vs. Google BigQuery vs. Hypertable vs. IRONdb

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Editorial information provided by DB-Engines

Name

Hypertable has stopped its further development with March 2016 and is removed from the DB-Engines ranking.

IRONdb seems to be discontinued. Therefore it is excluded 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

Large scale data warehouse service with append-only tables

An open source BigTable implementation based on distributed file systems such as Hadoop

A distributed Time Series DBMS with a focus on scalability, fault tolerance and operational simplicity

Primary database model

Key-value store
Relational DBMS

Relational DBMS

Wide column store

Time Series DBMS

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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Website

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

www.circonus.com/solutions/time-series-database/

Technical documentation

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

cloud.google.com/bigquery/docs

docs.circonus.com/irondb/category/getting-started

Developer

FairCom Corporation

Google

Hypertable Inc.

Circonus LLC.

Initial release

1979

2010

2009

2017

Current release

V3, October 2020

0.9.8.11, March 2016

V0.10.20, January 2018

License

Commercial or Open Source

commercial

Restricted, free version available

commercial

Open Source

GNU version 3. Commercial license available

commercial

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

ANSI C, C++

C++

C and C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Windows

an inofficial Windows port is available

Linux

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes

text, numeric, histograms

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

restricted

only exact value or prefix value scans

SQL

Support of SQL

yes

ANSI SQL queries

yes

SQL-like query language (Circonus Analytics Query Language: CAQL)

APIs and other access methods

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

RESTful HTTP/JSON API

C++ API
Thrift

HTTP API

Supported programming languages

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
Perl
PHP
Python
Ruby

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

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

yes, in Lua

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

File partitioning

Customizable business rules for partitioning

none

Sharding

Automatic, metric affinity per node

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor on file system level

configurable replication factor, datacenter aware

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency

Immediate consistency per node, eventual consistency across nodes

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

across SQL and NoSQL

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.

yes

User concepts

Access control

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

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