DBMS > Amazon Redshift vs. Databricks vs. Derby vs. Hawkular Metrics

System Properties Comparison Amazon Redshift vs. Databricks vs. Derby vs. Hawkular Metrics

Please select another system to include it in the comparison.

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.

Full-featured RDBMS with a small footprint, either embedded into a Java application or used as a database server.

Hawkular metrics is the metric storage of the Red Hat sponsored Hawkular monitoring system. It is based on Cassandra.

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

Time Series DBMS

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

Score	19.03
Rank	#34	Overall
	#21	Relational DBMS

Score	76.33
Rank	#17	Overall
	#3	Document stores
	#11	Relational DBMS

Score	5.30
Rank	#66	Overall
	#36	Relational DBMS

Score	0.04
Rank	#374	Overall
	#38	Time Series DBMS

Website

aws.amazon.com/redshift

www.databricks.com

db.apache.org/derby

www.hawkular.org

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

db.apache.org/derby/manuals/index.html

www.hawkular.org/hawkular-metrics/docs/user-guide

Developer

Amazon (based on PostgreSQL)

Databricks

Apache Software Foundation

Community supported by Red Hat

Initial release

2012

2013

1997

2014

Current release

10.17.1.0, November 2023

License

Commercial or Open Source

commercial

Open Source

Apache version 2

Open Source

Apache 2.0

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

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

Data scheme

yes

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

yes

schema-free

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

with Databricks SQL

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC

HTTP REST

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

Java

Go
Java
Python
Ruby

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

Java Stored Procedures

Triggers

yes

via Hawkular Alerting

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

based on Cassandra

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

selectable replication factor

based on Cassandra

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

yes

Transaction concepts

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

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

fine grained access rights according to SQL-standard

More information provided by the system vendor

Specific characteristics

Supported database models : In addition to the Document store and Relational DBMS...
» more

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.