DBMS > Altibase vs. Drizzle vs. Google BigQuery

System Properties Comparison Altibase 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

An enterprise grade, high-performance RDBMS

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

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

Score	0.87
Rank	#200	Overall
	#93	Relational DBMS

Score	52.67
Rank	#19	Overall
	#13	Relational DBMS

Website

altibase.com

cloud.google.com/bigquery

Technical documentation

github.com/ALTIBASE/Documents/tree/master/Manuals

cloud.google.com/bigquery/docs

Developer

Altibase

Drizzle project, originally started by Brian Aker

Google

Initial release

1999

2008

2010

Current release

v7.3, 2023, August 2023

7.2.4, September 2012

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

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++

Server operating systems

AIX
HP-UX
Linux

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

ANSI SQL-92

yes

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC

RESTful HTTP/JSON API

Supported programming languages

C
C++
Java

C
C++
Java
PHP

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

stored procedures and stored functions

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

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

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.

yes

User concepts

Access control

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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