DBMS > atoti vs. Faircom EDGE vs. Google Cloud Datastore vs. OpenMLDB vs. searchxml

System Properties Comparison atoti vs. Faircom EDGE vs. Google Cloud Datastore vs. OpenMLDB vs. searchxml

Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

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

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

An open-source machine learning database that provides a feature platform for training and inference

DBMS for structured and unstructured content wrapped with an application server

Primary database model

Object oriented DBMS

Key-value store
Relational DBMS

Document store

Time Series DBMS

Native XML DBMS
Search engine

Secondary database models

Relational DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	0.03
Rank	#390	Overall
	#7	Native XML DBMS
	#24	Search engines

Website

atoti.io

www.faircom.com/products/faircom-edge

cloud.google.com/datastore

openmldb.ai

www.searchxml.net/category/products

Technical documentation

docs.atoti.io

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

cloud.google.com/datastore/docs

openmldb.ai/docs/zh/main

www.searchxml.net/support/handouts

Developer

ActiveViam

FairCom Corporation

Google

4 Paradigm Inc.

informationpartners gmbh

Initial release

1979

2008

2020

2015

Current release

V3, October 2020

2024-2 February 2024

1.0

License

Commercial or Open Source

commercial

free versions available

commercial

Restricted, free version available

commercial

Open Source

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

Java

ANSI C, C++

C++, Java, Scala

C++

Server operating systems

Android
Linux

ARM, x86
Raspbian
Windows

hosted

Linux

Windows

Data scheme

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

schema-free

Fixed schema

schema-free

Typing

predefined data types such as float or date

yes, ANSI Standard SQL Types

yes, details here

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

Multidimensional Expressions (MDX)

yes

ANSI SQL queries

SQL-like query language (GQL)

yes

APIs and other access methods

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

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
SQLAlchemy

RESTful HTTP API
WebDAV
XQuery
XSLT

Supported programming languages

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

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Go
Java
Python
Scala

C++

most other programming languages supported via APIs

Server-side scripts

Stored procedures

Python

yes

.Net, JavaScript, C/C++

using Google App Engine

yes

on the application server

Triggers

yes

Callbacks using the Google Apps Engine

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

File partitioning

Customizable business rules for partitioning

Sharding

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Synchronous and asynchronous realtime replication based on transaction logs

Multi-source replication using Paxos

Source-replica replication

yes

sychronisation to multiple collections

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency
Tunable Consistency

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency

Foreign keys

Referential integrity

yes

when using SQL

yes

via ReferenceProperties or Ancestor paths

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

multiple readers, single writer

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

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 rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

fine grained access rights according to SQL-standard

Domain, group and role-based access control at the document level and for application services

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