DBMS > Databricks vs. NSDb vs. Oracle NoSQL vs. VoltDB

System Properties Comparison Databricks vs. NSDb vs. Oracle NoSQL vs. VoltDB

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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.

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Document store
Relational DBMS

Time Series DBMS

Document store
Key-value store
Relational DBMS

Relational 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	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

www.databricks.com

nsdb.io

www.oracle.com/database/nosql/technologies/nosql

www.voltdb.com

Technical documentation

docs.databricks.com

nsdb.io/Architecture

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.voltdb.com

Developer

Databricks

Oracle

VoltDB Inc.

Initial release

2013

2017

2011

2010

Current release

23.3, December 2023

11.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Java, Scala

Java

Java, C++

Server operating systems

hosted

Linux
macOS

Linux
Solaris SPARC/x86

Linux
OS X

for development

Data scheme

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

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

Typing

predefined data types such as float or date

yes: int, bigint, decimal, string

optional

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

all fields are automatically indexed

yes

SQL

Support of SQL

with Databricks SQL

SQL-like query language

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

gRPC
HTTP REST
WebSocket

RESTful HTTP API

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

Python
R
Scala

Java
Scala

C
C#
Go
Java
JavaScript (Node.js)
Python

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

user defined functions and aggregates

Java

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Eventual Consistency
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

Snapshots and command logging

In-memory capabilities

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

yes

off heap cache

User concepts

Access control

Access rights for users and roles

Users and roles with access to stored procedures

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

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