DBMS > Databricks vs. EsgynDB vs. Greenplum vs. Stardog

System Properties Comparison Databricks vs. EsgynDB vs. Greenplum vs. Stardog

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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.

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

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Relational DBMS

Relational DBMS

Graph DBMS
RDF store

Secondary database models

Document store
Spatial DBMS

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

Score	86.46
Rank	#14	Overall
	#2	Document stores
	#9	Relational DBMS

Score	0.15
Rank	#325	Overall
	#145	Relational DBMS

Score	6.85
Rank	#51	Overall
	#30	Relational DBMS

Score	1.78
Rank	#130	Overall
	#11	Graph DBMS
	#6	RDF stores

Website

Technical documentation

docs.databricks.com

docs.greenplum.org

docs.stardog.com

Developer

Databricks

Esgyn

Pivotal Software Inc.

Stardog-Union

Initial release

2013

2015

2005

2010

Current release

7.0.0, September 2023

7.3.0, May 2020

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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

Java

Server operating systems

hosted

Linux

Linux
macOS
Windows

Data scheme

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

yes

schema-free and OWL/RDFS-schema support

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

since Version 4.2

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

with Databricks SQL

yes

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
ODBC

JDBC
ODBC

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

Python
R
Scala

All languages supporting JDBC/ODBC/ADO.Net

C
Java
Perl
Python
R

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

user defined functions and aggregates

Java Stored Procedures

yes

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication between multi datacenters

Source-replica replication

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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...
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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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Recent citations in the news

Databricks could launch IPO in two months but biding time despite investor pressure, CEO says
13 September 2024, ION Analytics

Exclusive | Amazon, Databricks Strike Five-Year Deal Around AI Chips
15 October 2024, The Wall Street Journal

Databricks Mosaic Research Examines Long-Context Retrieval-Augmented Generation: How Leading AI Models Handle Expansive Information for Improved Response Accuracy
9 November 2024, MarkTechPost

Databricks partners with AWS to accelerate GenAI development
16 October 2024, Blocks & Files

Databricks & AWS Join in Push to Enhance Gen AI Capabilities
17 October 2024, Technology Magazine

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9 September 2019, AWS Blog

EMC and Greenplum Dress Elephant for IT Parade
8 December 2011, WIRED

SAS To Offer High-Performance Analytics On EMC Greenplum Database Appliance
2 February 2017, Firstpost

We Did Not Intend to Take an Aggressive Stance: Greenplum’s Take on Hadoop War
27 February 2013, SiliconANGLE News

Has the time finally come for PostgreSQL?
20 April 2018, ZDNet

provided by Google News

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