DBMS > Axibase vs. Firebird vs. Google BigQuery vs. Hyprcubd vs. Ingres

System Properties Comparison Axibase vs. Firebird vs. Google BigQuery vs. Hyprcubd vs. Ingres

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Scalable TimeSeries DBMS based on HBase with integrated rule engine and visualization

Firebird is an open source RDBMS forked from Borland's InterBase

Large scale data warehouse service with append-only tables

Serverless Time Series DBMS

Well established RDBMS

Primary database model

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

Score	0.35
Rank	#282	Overall
	#25	Time Series DBMS

Score	20.50
Rank	#30	Overall
	#18	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	3.80
Rank	#82	Overall
	#44	Relational DBMS

Website

axibase.com/docs/atsd/finance

www.firebirdsql.org

cloud.google.com/bigquery

hyprcubd.com (offline)

www.actian.com/databases/ingres

Technical documentation

www.firebirdsql.org/en/reference-manuals

cloud.google.com/bigquery/docs

docs.actian.com/ingres

Developer

Axibase Corporation

Firebird Foundation

Google

Hyprcubd, Inc.

Actian Corporation

Initial release

2013

2000

As fork of Borland's InterBase

2010

1974

originally developed at University Berkely in early 1970s

Current release

15585

5.0.0, January 2024

11.2, May 2022

License

Commercial or Open Source

commercial

Community Edition (single node) is free, Enterprise Edition (distributed) is paid

Open Source

Initial Developer's Public License

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

C and C++

Server operating systems

Linux

AIX
FreeBSD
HP-UX
Linux
OS X
server-less

Firebird Embedded Server
Solaris
Unix
Windows

hosted

AIX
HP Open VMS
HP-UX
Linux
Solaris
Windows

Data scheme

yes

Typing

predefined data types such as float or date

yes

short, integer, long, float, double, decimal, string

yes

time, int, uint, float, string

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.

but tools for importing/exporting data from/to XML-files available

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

yes

SQL-like query language

yes

APIs and other access methods

JDBC
Proprietary protocol (Network API)
RESTful HTTP API

ADO.NET
C/C++ API
JDBC

Jaybird
ODBC
OLE DB

RESTful HTTP/JSON API

gRPC (https)

.NET Client API
JDBC
ODBC
proprietary protocol (OpenAPI)

Supported programming languages

Go
Java
PHP
Python
R
Ruby

C
C#
C++
Delphi
Java
JavaScript

Node.js
Lua
Perl
PHP
Python
Ruby

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Server-side scripts

Stored procedures

yes

PSQL

user defined functions

in JavaScript

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning

Ingres Star to access multiple databases simultaneously

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Ingres Replicator

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Features a multi-generational MVCC architecture, readers do not block writers

yes

MVCC

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

Users with fine-grained authorization concept

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

Google Cloud Identity & Access Management (IAM)

token access

fine grained access rights according to SQL-standard

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