DBMS > Databricks vs. Derby vs. FileMaker vs. PostGIS

System Properties Comparison Databricks vs. Derby vs. FileMaker vs. PostGIS

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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.

FileMaker is a cross-platform RDBMS that includes a GUI frontend.

Spatial extension of PostgreSQL

Primary database model

Document store
Relational DBMS

Relational DBMS

Spatial DBMS

Secondary database models

Relational DBMS

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

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

Score	4.71
Rank	#69	Overall
	#37	Relational DBMS

Score	48.20
Rank	#20	Overall
	#14	Relational DBMS

Score	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Website

www.databricks.com

db.apache.org/derby

www.claris.com/filemaker

postgis.net

Technical documentation

docs.databricks.com

db.apache.org/derby/manuals/index.html

www.claris.com/resources/documentation

postgis.net/documentation

Developer

Databricks

Apache Software Foundation

Claris

a subsidiary of Apple

Initial release

2013

1997

1983

2005

Current release

10.17.1.0, November 2023

19.4.1, November 2021

3.4.2, February 2024

License

Commercial or Open Source

commercial

Open Source

Apache version 2

commercial

Open Source

GPL v2.0

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

iOS

client part only
Linux
OS X
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

A http query request can return the data in XML format

yes

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

via plugins

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC

Filemaker WebDirect
JDBC
ODBC

Supported programming languages

Python
R
Scala

Java

PHP

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

yes

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

based on PostgreSQL

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

Source-replica replication, warm standby

since Version 14

yes

based on PostgreSQL

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

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

simple rights management via user accounts and connection to external directory services

yes

based on PostgreSQL

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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More resources

DB-Engines blog posts

PostgreSQL is the DBMS of the Year 2023
2 January 2024, Matthias Gelbmann, Paul Andlinger

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Spatial database management systems
6 April 2021, Matthias Gelbmann

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