DBMS > Amazon Redshift vs. Databricks vs. Drizzle vs. Sadas Engine vs. Splice Machine

System Properties Comparison Amazon Redshift vs. Databricks vs. Drizzle vs. Sadas Engine vs. Splice Machine

Editorial information provided by DB-Engines

Name

Drizzle has published its last release in September 2012. The open-source project is discontinued and Drizzle is excluded from the DB-Engines ranking.

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.

MySQL fork with a pluggable micro-kernel and with an emphasis of performance over compatibility.

SADAS Engine is a columnar DBMS specifically designed for high performance in data warehouse environments

Open-Source SQL RDBMS for Operational and Analytical use cases with native Machine Learning, powered by Hadoop and Spark

Primary database model

Relational DBMS

Document store
Relational DBMS

Relational DBMS

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	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	0.54
Rank	#250	Overall
	#114	Relational DBMS

Website

aws.amazon.com/redshift

www.databricks.com

www.sadasengine.com

splicemachine.com

Technical documentation

docs.aws.amazon.com/redshift

docs.databricks.com

www.sadasengine.com/en/sadas-engine-download-free-trial-and-documentation/#documentation

splicemachine.com/how-it-works

Developer

Amazon (based on PostgreSQL)

Databricks

Drizzle project, originally started by Brian Aker

SADAS s.r.l.

Splice Machine

Initial release

2012

2013

2008

2006

2014

Current release

7.2.4, September 2012

8.0

3.1, March 2021

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

free trial version available

Open Source

AGPL 3.0, commercial license available

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

C++

Java

Server operating systems

hosted

FreeBSD
Linux
OS X

AIX
Linux
Windows

Linux
OS X
Solaris
Windows

Data scheme

yes

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

yes

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

with proprietary extensions

yes

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
RESTful HTTP API

JDBC

JDBC
ODBC
Proprietary protocol

JDBC
Native Spark Datasource
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

Python
R
Scala

C
C++
Java
PHP

.Net
C
C#
C++
Groovy
Java
PHP
Python

C#
C++
Java
JavaScript (Node.js)
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions and aggregates

yes

Java

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

Shared Nothhing Auto-Sharding, Columnar Partitioning

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Yes, via Full Spark Integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

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

yes, multi-version concurrency control (MVCC)

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

managed by 'Learn by Usage'

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Access rights for users, groups and roles 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...
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