DBMS > Databricks vs. FileMaker vs. Lovefield vs. SAP HANA

System Properties Comparison Databricks vs. FileMaker vs. Lovefield vs. SAP HANA

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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.

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

Embeddable relational database for web apps written in pure JavaScript

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Document store
Relational DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
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	47.91
Rank	#20	Overall
	#14	Relational DBMS

Score	0.33
Rank	#286	Overall
	#131	Relational DBMS

Score	44.27
Rank	#23	Overall
	#16	Relational DBMS

Website

www.databricks.com

www.claris.com/filemaker

google.github.io/lovefield

www.sap.com/products/hana.html

Technical documentation

docs.databricks.com

www.claris.com/resources/documentation

github.com/google/lovefield/blob/master/docs/spec_index.md

help.sap.com/hana

Developer

Databricks

Claris

a subsidiary of Apple

Google

SAP

Initial release

2013

1983

2014

2010

Current release

19.4.1, November 2021

2.1.12, February 2017

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

JavaScript

Server operating systems

hosted

iOS

client part only
Linux
OS X
Windows

server-less, requires a JavaScript environment (browser, Node.js)

tested with Chrome, Firefox, IE, Safari

Appliance or cloud-service

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

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

via plugins

SQL-like query language

via JavaScript builder pattern

yes

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

Filemaker WebDirect
JDBC
ODBC

JDBC
ODBC

Supported programming languages

Python
R
Scala

PHP

JavaScript

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

SQLScript, R

Triggers

yes

Using read-only observers

yes

Partitioning methods

Methods for storing different data on different nodes

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication, warm standby

since Version 14

none

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, by using IndexedDB or the cloud service Firebase Realtime Database

yes

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

using MemoryDB

yes

User concepts

Access control

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

yes

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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