DBMS > Databricks vs. PostGIS vs. SAP HANA vs. StarRocks

System Properties Comparison Databricks vs. PostGIS vs. SAP HANA vs. StarRocks

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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.

Spatial extension of PostgreSQL

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

An open source, high-performance columnar analytical database that enables real-time, multi-dimensional, and highly concurrent data analytics

Forked from Apache Doris

Primary database model

Document store
Relational DBMS

Spatial DBMS

Relational DBMS

Secondary database models

Relational DBMS

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial 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	22.69
Rank	#29	Overall
	#1	Spatial DBMS

Score	44.69
Rank	#22	Overall
	#16	Relational DBMS

Score	1.04
Rank	#189	Overall
	#88	Relational DBMS

Website

www.databricks.com

postgis.net

www.sap.com/products/hana.html

www.starrocks.io

Technical documentation

docs.databricks.com

postgis.net/documentation

help.sap.com/hana

docs.starrocks.io/en-us/latest/introduction/StarRocks_intro

Developer

Databricks

SAP

The Linux Foundation

since Feb 2023

Initial release

2013

2005

2010

2020

Current release

3.4.2, February 2024

2.0 SPS07 (April 4, 2023), April 2023

2.5.3, March 2023

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

commercial

Open Source

Apache Version 2.0

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

C++, Java

Server operating systems

hosted

Appliance or cloud-service

Linux

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

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JDBC
ODBC

JDBC
MySQL protocol

Supported programming languages

Python
R
Scala

Java

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

SQLScript, R

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

based on PostgreSQL

yes

horizontal partitioning (by range and hash)

Replication methods

Methods for redundantly storing data on multiple nodes

yes

based on PostgreSQL

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

In-memory capabilities

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

yes

User concepts

Access control

yes

based on PostgreSQL

yes

Role based access control and fine grained access rights

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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