DBMS > Altibase vs. Databricks vs. OpenQM vs. RocksDB

System Properties Comparison Altibase vs. Databricks vs. OpenQM vs. RocksDB

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Editorial information provided by DB-Engines

Name

Description

An enterprise grade, high-performance RDBMS

The Databricks Lakehouse Platform combines elements of data lakes and data warehouses to provide a unified view onto structured and unstructured data. It is based on Apache Spark.

QpenQM is a high-performance, self-tuning, multi-value DBMS

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

Primary database model

Relational DBMS

Document store
Relational DBMS

Multivalue DBMS

Key-value store

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

Score	1.07
Rank	#186	Overall
	#87	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	0.27
Rank	#298	Overall
	#10	Multivalue DBMS

Score	3.65
Rank	#85	Overall
	#11	Key-value stores

Website

altibase.com

www.databricks.com

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-open-qm

rocksdb.org

Technical documentation

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

docs.databricks.com

github.com/facebook/rocksdb/wiki

Developer

Altibase

Databricks

Rocket Software, originally Martin Phillips

Facebook, Inc.

Initial release

1999

2013

1993

2013

Current release

v7.3, 2023, August 2023

3.4-12

8.11.4, April 2024

License

Commercial or Open Source

commercial

Open source edition discontinued with March 2023

commercial

Open Source

GPLv2, extended commercial license available

Open Source

BSD

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

C++

Server operating systems

AIX
HP-UX
Linux

hosted

AIX
FreeBSD
Linux
macOS
Raspberry Pi
Solaris
Windows

Linux

Data scheme

yes

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

yes

with some exceptions

schema-free

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.

yes

Secondary indexes

yes

SQL

Support of SQL

ANSI SQL-92

with Databricks SQL

APIs and other access methods

JDBC
ODBC
Proprietary protocol

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Supported programming languages

C
C++
Java

Python
R
Scala

.Net
Basic
C
Java
Objective C
PHP
Python

C
C++
Go
Java
Perl
Python
Ruby

Server-side scripts

Stored procedures

stored procedures and stored functions

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes

horizontal partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

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

yes

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 rights can be defined down to the item level

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
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