DBMS > Amazon Redshift vs. Databricks vs. Oracle NoSQL vs. XTDB

System Properties Comparison Amazon Redshift vs. Databricks vs. Oracle NoSQL vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

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

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Relational DBMS

Document store
Relational DBMS

Document store
Key-value store
Relational DBMS

Document store

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

Score	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	78.61
Rank	#15	Overall
	#2	Document stores
	#10	Relational DBMS

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

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

aws.amazon.com/redshift

www.databricks.com

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

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

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

www.xtdb.com/docs

Developer

Amazon (based on PostgreSQL)

Databricks

Oracle

Juxt Ltd.

Initial release

2012

2013

2011

2019

Current release

23.3, December 2023

1.19, September 2021

License

Commercial or Open Source

commercial

Open Source

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

Open Source

MIT License

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

Clojure

Server operating systems

hosted

Linux
Solaris SPARC/x86

All OS with a Java 8 (and higher) VM
Linux

Data scheme

yes

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

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

schema-free

Typing

predefined data types such as float or date

yes

optional

yes, extensible-data-notation format

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

with Databricks SQL

SQL-like DML and DDL statements

limited SQL, making use of Apache Calcite

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

RESTful HTTP API

HTTP REST
JDBC

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

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

Clojure
Java

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

Triggers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

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

yes, each node contains all data

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
Immediate Consistency

depending on configuration

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

Transaction concepts

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

ACID

configurable

ACID within a storage node (=shard)

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

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

fine grained access rights according to SQL-standard

Access rights for users and roles

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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