DBMS > Microsoft Azure Cosmos DB vs. Sadas Engine vs. Spark SQL vs. Teradata

System Properties Comparison Microsoft Azure Cosmos DB vs. Sadas Engine vs. Spark SQL vs. Teradata

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Editorial information provided by DB-Engines

Name

Description

Globally distributed, horizontally scalable, multi-model database service

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Primary database model

Document store
Graph DBMS
Key-value store
Wide column store

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	29.04
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

Score	0.00
Rank	#383	Overall
	#158	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Website

azure.microsoft.com/services/cosmos-db

www.sadasengine.com

spark.apache.org/sql

www.teradata.com

Technical documentation

learn.microsoft.com/azure/cosmos-db

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

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

docs.teradata.com

Developer

Microsoft

SADAS s.r.l.

Apache Software Foundation

Teradata

Initial release

2014

2006

2014

1984

Current release

8.0

3.5.0 ( 2.13), September 2023

Teradata Vantage 1.0 MU2, January 2019

License

Commercial or Open Source

commercial

free trial version 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

AIX
Linux
Windows

Linux
OS X
Windows

hosted
Linux

Data scheme

schema-free

yes

Typing

predefined data types such as float or date

yes

JSON types

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

yes

All properties auto-indexed by default

yes

Join-index to prejoin tables, aggregate index, sparse index, hash index

SQL

Support of SQL

SQL-like query language

yes

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

APIs and other access methods

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

JDBC
ODBC
Proprietary protocol

JDBC
ODBC

.NET Client API
HTTP REST
JDBC
JMS Adapter
ODBC
OLE DB

Supported programming languages

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

Java
Python
R
Scala

C
C++
Cobol
Java (JDBC-ODBC)
Perl
PL/1
Python
R
Ruby

Server-side scripts

Stored procedures

JavaScript

yes

UDFs, stored procedures, table functions in parallel

Triggers

JavaScript

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

horizontal partitioning

yes, utilizing Spark Core

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

Consistency concepts

Methods to ensure consistency in a distributed system

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

Multi-item ACID transactions with snapshot isolation within a partition

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

managed by 'Learn by Usage'

yes

User concepts

Access control

Access rights can be defined down to the item level

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

fine grained access rights according to SQL-standard

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