DBMS > Amazon Redshift vs. Databricks vs. Geode

System Properties Comparison Amazon Redshift vs. Databricks vs. Geode

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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.

Geode is a distributed data container, pooling memory, CPU, network resources, and optionally local disk across multiple processes

Primary database model

Relational DBMS

Document store
Relational DBMS

Key-value 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	1.92
Rank	#131	Overall
	#23	Key-value stores

Website

aws.amazon.com/redshift

www.databricks.com

geode.apache.org

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

geode.apache.org/docs

Developer

Amazon (based on PostgreSQL)

Databricks

Originally developed by Gemstone. They outsourced the project to Apache in 2015 but still deliver a commercial version as Gemfire.

Initial release

2012

2013

2002

Current release

1.1, February 2017

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available as Gemfire

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java

Server operating systems

hosted

All OS with a Java VM

the JDK (8 or later) is also required

Data scheme

yes

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

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

SQL-like query language (OQL)

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

Java Client API
Memcached protocol
RESTful HTTP API

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

.Net
All JVM based languages
C++
Groovy
Java
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

user defined functions

Triggers

yes

Cache Event Listeners

Partitioning methods

Methods for storing different data on different nodes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

yes, on a single node

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

Access rights per client and object definable

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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