DBMS > Databricks vs. H2 vs. HyperSQL vs. MonetDB

System Properties Comparison Databricks vs. H2 vs. HyperSQL vs. MonetDB

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Editorial information provided by DB-Engines

Name

Description

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Multithreaded, transactional RDBMS written in Java

also known as HSQLDB

A relational database management system that stores data in columns

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	8.33
Rank	#46	Overall
	#30	Relational DBMS

Score	3.23
Rank	#93	Overall
	#48	Relational DBMS

Score	1.72
Rank	#141	Overall
	#64	Relational DBMS

Website

Technical documentation

docs.databricks.com

www.h2database.com/html/main.html

hsqldb.org/web/hsqlDocsFrame.html

www.monetdb.org/Documentation

Developer

Databricks

Thomas Mueller

MonetDB BV

Initial release

2013

2005

2001

2004

Current release

2.2.220, July 2023

2.7.2, June 2023

Dec2023 (11.49), December 2023

License

Commercial or Open Source

commercial

Open Source

dual-licence (Mozilla public license, Eclipse public license)

Open Source

based on BSD license

Open Source

Mozilla Public License 2.0

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

Server operating systems

hosted

All OS with a Java VM

Embedded (into Java applications) and Client-Server operating modes

FreeBSD
Linux
OS X
Solaris
Windows

Data scheme

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

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.

yes

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

SQL 2003 with some extensions

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

HTTP API

JDBC via HTTP
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

Supported programming languages

Python
R
Scala

Java

All languages supporting JDBC/ODBC
Java

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

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures and User-Defined Functions

Java, SQL

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

Sharding via remote tables

Replication methods

Methods for redundantly storing data on multiple nodes

yes

With clustering: 2 database servers on different computers operate on identical copies of a database

none

Source-replica replication available in experimental status

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

Concurrency

Support for concurrent manipulation of data

yes

yes, multi-version concurrency control (MVCC)

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

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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