DBMS > Databricks vs. EsgynDB vs. Oracle Berkeley DB vs. ToroDB

System Properties Comparison Databricks vs. EsgynDB vs. Oracle Berkeley DB vs. ToroDB

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

ToroDB seems to be discontinued. Therefore it is excluded from the DB-Engines Ranking.

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.

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

Widely used in-process key-value store

A MongoDB-compatible JSON document store, built on top of PostgreSQL

Primary database model

Document store
Relational DBMS

Relational DBMS

Key-value store

supports sorted and unsorted key sets
Native XML DBMS

in the Oracle Berkeley DB XML version

Document store

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	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	2.21
Rank	#117	Overall
	#20	Key-value stores
	#3	Native XML DBMS

Website

www.databricks.com

www.esgyn.cn

www.oracle.com/database/technologies/related/berkeleydb.html

github.com/torodb/server

Technical documentation

docs.databricks.com

docs.oracle.com/cd/E17076_05/html/index.html

Developer

Databricks

Esgyn

Oracle

originally developed by Sleepycat, which was acquired by Oracle

8Kdata

Initial release

2013

2015

1994

2016

Current release

18.1.40, May 2020

License

Commercial or Open Source

commercial

Open Source

commercial license available

Open Source

AGPL-V3

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++, Java

C, Java, C++ (depending on the Berkeley DB edition)

Java

Server operating systems

hosted

Linux

AIX
Android
FreeBSD
iOS
Linux
OS X
Solaris
VxWorks
Windows

All OS with a Java 7 VM

Data scheme

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

yes

schema-free

Typing

predefined data types such as float or date

yes

string, integer, double, boolean, date, object_id

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

only with the Berkeley DB XML edition

Secondary indexes

yes

SQL

Support of SQL

with Databricks SQL

yes

SQL interfaced based on SQLite is available

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

Supported programming languages

Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

.Net

Figaro is a .Net framework assembly that extends Berkeley DB XML into an embeddable database engine for .NET
others

Third-party libraries to manipulate Berkeley DB files are available for many languages
C
C#
C++
Java
JavaScript (Node.js)

3rd party binding
Perl
Python
Tcl

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

Triggers

yes

only for the SQL API

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

Source-replica replication

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

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 for users and roles

More information provided by the system vendor

Specific characteristics

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

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources

DB-Engines blog posts

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

show all

Recent citations in the news

Exclusive | Pete Sonsini, Early Investor in Databricks, Gets Closer to Launching New VC Firm
3 May 2024, The Wall Street Journal

Databricks expands Seattle presence with office in West 8th - Puget Sound Business Journal
2 May 2024, The Business Journals

Protecting Your AI Investments: Databricks' Breakthrough Security Framework
2 May 2024, Acceleration Economy

Databricks on AWS GovCloud Secures FedRAMP High Authority to Operate; Aaron Kinworthy Quoted
1 May 2024, ExecutiveBiz

Tableau adds generative AI tools, tightens Databricks bond
30 April 2024, TechTarget

provided by Google News

Margo Seltzer Named ACM Athena Lecturer for Technical and Mentoring Contributions
26 April 2023, HPCwire

EC will investigate the Oracle/Sun takeover due to concerns about MySQL
3 September 2009, The Guardian

Database Trends Report: SQL Beats NoSQL, MySQL Most Popular -- ADTmag
5 March 2019, ADT Magazine

The importance of bitcoin nodes and how to start one
9 May 2014, The Merkle News

A Quick Look at Open Source Databases for Mobile App Development
29 April 2018, Open Source For You

provided by Google News

Share this page