DBMS > Drizzle vs. Spark SQL vs. Teradata vs. Valentina Server

System Properties Comparison Drizzle vs. Spark SQL vs. Teradata vs. Valentina Server

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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

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

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

A hybrid cloud data analytics software platform (Teradata Vantage)

Object-relational database and reports server

Primary database model

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS
Time Series DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	45.33
Rank	#21	Overall
	#15	Relational DBMS

Score	0.17
Rank	#327	Overall
	#145	Relational DBMS

Website

spark.apache.org/sql

www.teradata.com

www.valentina-db.net

Technical documentation

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

docs.teradata.com

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

Developer

Drizzle project, originally started by Brian Aker

Apache Software Foundation

Teradata

Paradigma Software

Initial release

2008

2014

1984

1999

Current release

7.2.4, September 2012

3.5.0 ( 2.13), September 2023

Teradata Vantage 1.0 MU2, January 2019

5.7.5

License

Commercial or Open Source

Open Source

GNU GPL

Open Source

Apache 2.0

commercial

Cloud-based only

Only available as a cloud service

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

FreeBSD
Linux
OS X

Linux
OS X
Windows

hosted
Linux

Linux
OS X
Windows

Data scheme

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

yes

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

yes

SQL

Support of SQL

yes

with proprietary extensions

SQL-like DML and DDL statements

yes

SQL 2016 + extensions

yes

APIs and other access methods

JDBC

JDBC
ODBC

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

ODBC

Supported programming languages

C
C++
Java
PHP

Java
Python
R
Scala

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

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

Server-side scripts

Stored procedures

yes

UDFs, stored procedures, table functions in parallel

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

Sharding

Hashing

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication
Source-replica replication

none

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

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

User concepts

Access control

Pluggable authentication mechanisms

e.g. LDAP, HTTP

fine grained access rights according to SQL-standard

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More resources

DB-Engines blog posts

MySQL won the April ranking; did its forks follow?
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