DBMS > Ehcache vs. Faircom EDGE vs. Google BigQuery vs. KairosDB

System Properties Comparison Ehcache vs. Faircom EDGE vs. Google BigQuery vs. KairosDB

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Editorial information provided by DB-Engines

Name

Description

A widely adopted Java cache with tiered storage options

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

Distributed Time Series DBMS based on Cassandra or H2

Primary database model

Key-value store

Key-value store
Relational DBMS

Relational DBMS

Time Series DBMS

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

Score	4.89
Rank	#67	Overall
	#8	Key-value stores

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.62
Rank	#239	Overall
	#20	Time Series DBMS

Website

www.ehcache.org

www.faircom.com/products/faircom-edge

cloud.google.com/bigquery

github.com/kairosdb/kairosdb

Technical documentation

www.ehcache.org/documentation

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

cloud.google.com/bigquery/docs

kairosdb.github.io

Developer

Terracotta Inc, owned by Software AG

FairCom Corporation

Google

Initial release

2009

1979

2010

2013

Current release

3.10.0, March 2022

V3, October 2020

1.2.2, November 2018

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Restricted, free version available

commercial

Open Source

Apache 2.0

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

Java

ANSI C, C++

Java

Server operating systems

All OS with a Java VM

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux
OS X
Windows

Data scheme

schema-free

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

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, ANSI Standard SQL Types

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

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL queries

yes

APIs and other access methods

JCache

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

RESTful HTTP/JSON API

Graphite protocol
HTTP REST
Telnet API

Supported programming languages

Java

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

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

.Net, JavaScript, C/C++

user defined functions

in JavaScript

Triggers

yes

Cache Event Listeners

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

by using Terracotta Server

File partitioning

Customizable business rules for partitioning

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

by using Terracotta Server

yes

Synchronous and asynchronous realtime replication based on transaction logs

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Tunable Consistency (Strong, Eventual, Weak)

Immediate Consistency
Tunable Consistency

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

when using SQL

Transaction concepts

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

yes

supports JTA and can work as an XA resource

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

using a tiered cache-storage approach

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)

simple password-based access control

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