DBMS > Greenplum vs. Spark SQL vs. Teradata Aster vs. VoltDB

System Properties Comparison Greenplum vs. Spark SQL vs. Teradata Aster vs. VoltDB

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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

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

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

Distributed In-Memory NewSQL RDBMS

Used for OLTP applications with a high frequency of relatively simple transactions, that can hold all their data in memory

Primary database model

Relational DBMS

Secondary database models

Document store
Spatial DBMS

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

Score	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.44
Rank	#158	Overall
	#73	Relational DBMS

Website

greenplum.org

spark.apache.org/sql

www.voltdb.com

Technical documentation

docs.greenplum.org

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

docs.voltdb.com

Developer

Pivotal Software Inc.

Apache Software Foundation

Teradata

VoltDB Inc.

Initial release

2005

2014

2005

2010

Current release

7.0.0, September 2023

3.5.0 ( 2.13), September 2023

11.3, April 2022

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

Apache 2.0

commercial

Open Source

AGPL for Community Edition, commercial license for Enterprise, AWS, and Pro Editions

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

Scala

Java, C++

Server operating systems

Linux

Linux
OS X
Windows

Linux

Linux
OS X

for development

Data scheme

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

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

since Version 4.2

yes

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

only a subset of SQL 99

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Java API
JDBC
RESTful HTTP/JSON API

Supported programming languages

C
Java
Perl
Python
R

Java
Python
R
Scala

C
C#
C++
Java
Python
R

C#
C++
Erlang

not officially supported
Go
Java
JavaScript

Node.js
PHP
Python

Server-side scripts

Stored procedures

yes

R packages

Java

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

none

yes

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

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

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

FOREIGN KEY constraints are not supported

Transaction concepts

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

ACID

Transactions are executed single-threaded within stored procedures

Concurrency

Support for concurrent manipulation of data

yes

Data access is serialized by the server

Durability

Support for making data persistent

yes

Snapshots and command logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

Users and roles with access to stored procedures

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More resources

Recent citations in the news

VMware Greenplum on AWS: Parallel Postgres for Enterprise Analytics at Scale | Amazon Web Services
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RSA: EMC integrates Hadoop with Greenplum database
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19 March 2019, ZDNet

Greenplum 6 review: Jack of all trades, master of some
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8 October 2013, ZDNet

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22 September 2011, PR Newswire

Teradata's Aster shows how the flowers of fraud bloom
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VoltDB Turns to Real-Time Analytics with NewSQL Database
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VoltDB Upgrades Power, Security of Its In-Memory Database
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