DBMS > Databricks vs. Hazelcast vs. NSDb vs. SWC-DB

System Properties Comparison Databricks vs. Hazelcast vs. NSDb 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.

A widely adopted in-memory data grid

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

A high performance, scalable Wide Column DBMS

Primary database model

Document store
Relational DBMS

Key-value store

Time Series DBMS

Wide column store

Secondary database models

Document store

JSON support with IMDG 3.12

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	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.01
Rank	#376	Overall
	#13	Wide column stores

Website

www.databricks.com

hazelcast.com

nsdb.io

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

Technical documentation

docs.databricks.com

hazelcast.org/imdg/docs

nsdb.io/Architecture

Developer

Databricks

Hazelcast

Alex Kashirin

Initial release

2013

2008

2017

2020

Current release

5.3.6, November 2023

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

Open Source

Apache Version 2.0

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

Java

Java, Scala

C++

Server operating systems

hosted

All OS with a Java VM

Linux
macOS

Linux

Data scheme

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

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

the object must implement a serialization strategy

Secondary indexes

yes

all fields are automatically indexed

SQL

Support of SQL

with Databricks SQL

SQL-like query language

APIs and other access methods

JDBC
ODBC
RESTful HTTP API

JCache
JPA
Memcached protocol
RESTful HTTP API

gRPC
HTTP REST
WebSocket

Proprietary protocol
Thrift

Supported programming languages

Python
R
Scala

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

Java
Scala

C++

Server-side scripts

Stored procedures

user defined functions and aggregates

yes

Event Listeners, Executor Services

Triggers

yes

Events

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

ACID

one or two-phase-commit; repeatable reads; read commited

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

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

Role-based access control

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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