DBMS > DataFS vs. MarkLogic vs. Snowflake vs. Spark SQL vs. Teradata Aster

System Properties Comparison DataFS vs. MarkLogic vs. Snowflake vs. Spark SQL vs. Teradata Aster

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

All data is stored inside objects which are linked by so-called link attributes. Objects consist of classes which can be extended and de-extended at runtime. Graphs can be defined with a struct.

Operational and transactional Enterprise NoSQL database

Cloud-based data warehousing service for structured and semi-structured data

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

Platform for big data analytics on multistructured data sources and types

Primary database model

Object oriented DBMS

Document store
Native XML DBMS
RDF store

as of version 7
Search engine

Relational DBMS

Secondary database models

Graph DBMS

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

Score	0.09
Rank	#360	Overall
	#17	Object oriented DBMS

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

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	18.04
Rank	#33	Overall
	#20	Relational DBMS

Website

Technical documentation

dev.mobiland.com/Overview.xsp

docs.marklogic.com

docs.snowflake.net/manuals/index.html

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

Developer

Mobiland AG

MarkLogic Corp.

Snowflake Computing Inc.

Apache Software Foundation

Teradata

Initial release

2018

2001

2014

2005

Current release

1.1.263, October 2022

11.0, December 2022

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

restricted free version is available

commercial

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

Windows

Linux
OS X
Windows

hosted

Linux
OS X
Windows

Linux

Data scheme

Classes, Structs, and Lists are written in proprietary DataTypeDefinitionLanguage (.dtdl) and Objects consisting of those are written in proprietary DataAccessDefinitionLanguage (.dadl)

schema-free

Schema can be enforced

yes

support of semi-structured data formats (JSON, XML, Avro)

yes

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

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

SQL92

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

.NET Client API
Proprietary client DLL
WinRT client

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

CLI Client
JDBC
ODBC

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
C
C#
C++
VB.Net

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

JavaScript (Node.js)
Python

Java
Python
R
Scala

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

yes

via XQuery or JavaScript

user defined functions

R packages

Triggers

no, except callback-events from server when changes happened

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Proprietary Sharding system

Sharding

yes

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

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

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, with Range Indexes

User concepts

Access control

Windows-Profile

Role-based access control at the document and subdocument levels

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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