DBMS > Databricks vs. GeoMesa vs. Oracle Rdb vs. WakandaDB

System Properties Comparison Databricks vs. GeoMesa vs. Oracle Rdb vs. WakandaDB

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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.

GeoMesa is a distributed spatio-temporal DBMS based on various systems as storage layer.

WakandaDB is embedded in a server that provides a REST API and a server-side javascript engine to access data

Primary database model

Document store
Relational DBMS

Spatial DBMS

Relational DBMS

Object oriented 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	0.78
Rank	#213	Overall
	#4	Spatial DBMS

Score	1.08
Rank	#184	Overall
	#85	Relational DBMS

Score	0.03
Rank	#364	Overall
	#17	Object oriented DBMS

Website

www.databricks.com

www.geomesa.org

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

wakanda.github.io

Technical documentation

docs.databricks.com

www.geomesa.org/documentation/stable/user/index.html

www.oracle.com/database/technologies/related/rdb-doc.html

wakanda.github.io/doc

Developer

Databricks

CCRi and others

Oracle, originally developed by Digital Equipment Corporation (DEC)

Wakanda SAS

Initial release

2013

2014

1984

2012

Current release

4.0.5, February 2024

7.4.1.1, 2021

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

Apache License 2.0

commercial

Open Source

AGPLv3, extended commercial license available

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

Scala

C++, JavaScript

Server operating systems

hosted

HP Open VMS

Linux
OS X
Windows

Data scheme

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

yes

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

RESTful HTTP API

Supported programming languages

Python
R
Scala

JavaScript

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

depending on storage layer

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

depending on storage layer

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

depending on storage layer

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

yes, on a single node

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.

depending on storage layer

User concepts

Access control

yes

depending on the DBMS used for storage

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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