DBMS > Apache Impala vs. Cubrid vs. Drizzle vs. Google BigQuery

System Properties Comparison Apache Impala vs. Cubrid vs. Drizzle vs. Google BigQuery

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Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

Description

Analytic DBMS for Hadoop

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

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

Large scale data warehouse service with append-only tables

Primary database model

Relational DBMS

Secondary database models

Document store

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

Score	12.45
Rank	#40	Overall
	#24	Relational DBMS

Score	1.04
Rank	#187	Overall
	#87	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Website

impala.apache.org

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

cloud.google.com/bigquery

Technical documentation

impala.apache.org/impala-docs.html

cubrid.org/manuals

cloud.google.com/bigquery/docs

Developer

Apache Software Foundation

Apache top-level project, originally developed by Cloudera

CUBRID Corporation, CUBRID Foundation

Drizzle project, originally started by Brian Aker

Google

Initial release

2013

2008

2010

Current release

4.1.0, June 2022

11.0, January 2021

7.2.4, September 2012

License

Commercial or Open Source

Open Source

Apache Version 2

Open Source

Apache Version 2.0

Open Source

GNU GPL

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, C++, Java

C++

Server operating systems

Linux

Linux
Windows

FreeBSD
Linux
OS X

hosted

Data scheme

yes

Typing

predefined data types such as float or date

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

SQL-like DML and DDL statements

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

JDBC

RESTful HTTP/JSON API

Supported programming languages

All languages supporting JDBC/ODBC

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

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

user defined functions and integration of map-reduce

Java Stored Procedures

user defined functions

in JavaScript

Triggers

yes

hooks for callbacks inside the server can be used.

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

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

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.

User concepts

Access control

Access rights for users, groups and roles

based on Apache Sentry and Kerberos

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

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

Google Cloud Identity & Access Management (IAM)

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