DBMS > Amazon Redshift vs. Hive vs. MarkLogic vs. Sadas Engine vs. Spark SQL

System Properties Comparison Amazon Redshift vs. Hive vs. MarkLogic vs. Sadas Engine vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

data warehouse software for querying and managing large distributed datasets, built on Hadoop

Operational and transactional Enterprise NoSQL database

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

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

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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	61.17
Rank	#18	Overall
	#12	Relational DBMS

Score	5.92
Rank	#58	Overall
	#10	Document stores
	#1	Native XML DBMS
	#1	RDF stores
	#6	Search engines

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/redshift

Technical documentation

docs.aws.amazon.com/redshift

cwiki.apache.org/confluence/display/Hive/Home

docs.marklogic.com

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

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

Developer

Amazon (based on PostgreSQL)

Apache Software Foundation

initially developed by Facebook

MarkLogic Corp.

SADAS s.r.l.

Apache Software Foundation

Initial release

2012

2001

2006

2014

Current release

3.1.3, April 2022

11.0, December 2022

8.0

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

restricted free version is available

commercial

free trial version available

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

C++

Scala

Server operating systems

hosted

All OS with a Java VM

Linux
OS X
Windows

AIX
Linux
Windows

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

SQL-like DML and DDL statements

yes

SQL92

yes

SQL-like DML and DDL statements

APIs and other access methods

JDBC
ODBC

JDBC
ODBC
Thrift

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
Proprietary protocol

JDBC
ODBC

Supported programming languages

All languages supporting JDBC/ODBC

C++
Java
PHP
Python

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

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

Java
Python
R
Scala

Server-side scripts

Stored procedures

user defined functions

in Python

yes

user defined functions and integration of map-reduce

yes

via XQuery or JavaScript

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

yes

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

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

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

managed by 'Learn by Usage'

User concepts

Access control

fine grained access rights according to SQL-standard

Access rights for users, groups and roles

Role-based access control at the document and subdocument levels

Access rights for users, groups and roles according to SQL-standard

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