DBMS > Databricks vs. PostGIS vs. RocksDB vs. SWC-DB

System Properties Comparison Databricks vs. PostGIS vs. RocksDB vs. SWC-DB

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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

Embeddable persistent key-value store optimized for fast storage (flash and RAM)

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Relational DBMS

Spatial DBMS

Key-value store

Wide column store

Secondary database models

Relational DBMS

Time Series 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	3.65
Rank	#85	Overall
	#11	Key-value stores

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.databricks.com

postgis.net

rocksdb.org

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

docs.databricks.com

postgis.net/documentation

github.com/facebook/rocksdb/wiki

Developer

Databricks

Facebook, Inc.

Alex Kashirin

Initial release

2013

2005

2013

2020

Current release

3.4.2, February 2024

8.11.4, April 2024

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

GPL v2.0

Open Source

BSD

Open Source

GPL 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++

Server operating systems

hosted

Linux

Data scheme

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

yes

schema-free

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

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

Proprietary protocol
Thrift

Supported programming languages

Python
R
Scala

C
C++
Go
Java
Perl
Python
Ruby

C++

Server-side scripts

Stored procedures

user defined functions and aggregates

user defined functions

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

yes

based on PostgreSQL

horizontal partitioning

Sharding

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

yes

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

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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