DBMS > Amazon Redshift vs. Drizzle vs. MarkLogic vs. Spark SQL vs. Valentina Server

System Properties Comparison Amazon Redshift vs. Drizzle vs. MarkLogic vs. Spark SQL vs. Valentina Server

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

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

Operational and transactional Enterprise NoSQL database

Spark SQL is a component on top of 'Spark Core' for structured data processing

Object-relational database and reports server

Primary database model

Relational DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

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

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	5.18
Rank	#63	Overall
	#11	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#7	Search engines

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Score	0.21
Rank	#325	Overall
	#144	Relational DBMS

Website

aws.amazon.com/redshift

www.progress.com/marklogic

spark.apache.org/sql

www.valentina-db.net

Technical documentation

docs.aws.amazon.com/redshift

www.progress.com/marklogic/documentation

spark.apache.org/docs/latest/sql-programming-guide.html

valentina-db.com/docs/dokuwiki/v5/doku.php

Developer

Amazon (based on PostgreSQL)

Drizzle project, originally started by Brian Aker

MarkLogic Corp.

Apache Software Foundation

Paradigma Software

Initial release

2012

2008

2001

2014

1999

Current release

7.2.4, September 2012

11.0, December 2022

3.5.0 ( 2.13), September 2023

5.7.5

License

Commercial or Open Source

commercial

Open Source

GNU GPL

commercial

restricted free version is available

Open Source

Apache 2.0

commercial

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++

Scala

Server operating systems

hosted

FreeBSD
Linux
OS X

Linux
OS X
Windows

Data scheme

yes

schema-free

Schema can be enforced

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

yes

with proprietary extensions

yes

SQL92

SQL-like DML and DDL statements

yes

APIs and other access methods

JDBC
ODBC

JDBC

Java API
Node.js Client API
ODBC
proprietary Optic API

Proprietary Query API, introduced with version 9
RESTful HTTP API
SPARQL
WebDAV
XDBC
XQuery
XSLT

JDBC
ODBC

ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C
C++
Java
PHP

C
C#
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
Ruby

Java
Python
R
Scala

.Net
C
C#
C++
Objective-C
PHP
Ruby
Visual Basic
Visual Basic.NET

Server-side scripts

Stored procedures

user defined functions

in Python

yes

via XQuery or JavaScript

yes

Triggers

hooks for callbacks inside the server can be used.

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication
Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

via Hadoop Connector, HDFS Direct Access and in-database MapReduce jobs

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

can act as a resource manager in an XA/JTA transaction

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

yes, with Range Indexes

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Pluggable authentication mechanisms

e.g. LDAP, HTTP

Role-based access control at the document and subdocument levels

fine grained access rights according to SQL-standard

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