DBMS > Databricks vs. RocksDB vs. STSdb vs. WakandaDB

System Properties Comparison Databricks vs. RocksDB vs. STSdb 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.

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

Key-Value Store with special method for indexing

optimized for high performance using a special indexing method

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

Key-value store

Object oriented DBMS

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

Score	81.08
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

Score	3.41
Rank	#86	Overall
	#11	Key-value stores

Score	0.10
Rank	#357	Overall
	#51	Key-value stores

Score	0.10
Rank	#356	Overall
	#16	Object oriented DBMS

Website

www.databricks.com

rocksdb.org

github.com/STSSoft/STSdb4

wakanda.github.io

Technical documentation

docs.databricks.com

github.com/facebook/rocksdb/wiki

wakanda.github.io/doc

Developer

Databricks

Facebook, Inc.

STS Soft SC

Wakanda SAS

Initial release

2013

2011

2012

Current release

9.2.1, May 2024

4.0.8, September 2015

2.7.0 (April 29, 2019), April 2019

License

Commercial or Open Source

commercial

Open Source

BSD

Open Source

GPLv2, commercial license available

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

C++

C++, JavaScript

Server operating systems

hosted

Linux

Windows

Linux
OS X
Windows

Data scheme

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

schema-free

yes

Typing

predefined data types such as float or date

yes

primitive types and user defined types (classes)

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

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

C++ API
Java API

.NET Client API

RESTful HTTP API

Supported programming languages

Python
R
Scala

C
C++
Go
Java
Perl
Python
Ruby

C#
Java

JavaScript

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

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

Transaction concepts

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

ACID

yes

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

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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