DBMS > Cubrid vs. Google BigQuery vs. Google Cloud Firestore vs. HBase vs. Kyligence Enterprise

System Properties Comparison Cubrid vs. Google BigQuery vs. Google Cloud Firestore vs. HBase vs. Kyligence Enterprise

Editorial information provided by DB-Engines

Name

Description

CUBRID is an open-source SQL-based relational database management system with object extensions for OLTP

Large scale data warehouse service with append-only tables

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Wide-column store based on Apache Hadoop and on concepts of BigTable

A distributed analytics engine for big data, built on top of Apache Kylin

Primary database model

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

Score	1.20
Rank	#169	Overall
	#78	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	30.50
Rank	#26	Overall
	#2	Wide column stores

Score	0.40
Rank	#269	Overall
	#124	Relational DBMS

Website

cubrid.com (korean)
cubrid.org (english)

cloud.google.com/bigquery

firebase.google.com/products/firestore

hbase.apache.org

kyligence.io/kyligence-enterprise

Technical documentation

cubrid.org/manuals

cloud.google.com/bigquery/docs

firebase.google.com/docs/firestore

hbase.apache.org/book.html

Developer

CUBRID Corporation, CUBRID Foundation

Google

Apache Software Foundation

Apache top-level project, originally developed by Powerset

Kyligence, Inc.

Initial release

2008

2010

2017

2008

2016

Current release

11.0, January 2021

2.3.4, January 2021

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Open Source

Apache version 2

commercial

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C, C++, Java

Java

Server operating systems

Linux
Windows

hosted

Linux
Unix
Windows

using Cygwin

Linux

Data scheme

yes

schema-free

schema-free, schema definition possible

yes

Typing

predefined data types such as float or date

yes

options to bring your own types, AVRO

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.

Secondary indexes

yes

SQL

Support of SQL

yes

ANSI SQL for queries (using Apache Calcite)

APIs and other access methods

ADO.NET
JDBC
ODBC
OLE DB

RESTful HTTP/JSON API

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

Java API
RESTful HTTP API
Thrift

JDBC
ODBC
RESTful HTTP API

Supported programming languages

C
C#
C++
Go
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

C
C#
C++
Groovy
Java
PHP
Python
Scala

Server-side scripts

Stored procedures

Java Stored Procedures

user defined functions

in JavaScript

yes, Firebase Rules & Cloud Functions

yes

Coprocessors in Java

Triggers

yes

yes, with Cloud Functions

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

yes

Single row ACID (across millions of columns)

Concurrency

Support for concurrent manipulation of data

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 access rights according to SQL-standard

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Access Control Lists (ACL) for RBAC, integration with Apache Ranger for RBAC & ABAC

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