DBMS > Spark SQL vs. Teradata Aster vs. TigerGraph

System Properties Comparison Spark SQL vs. Teradata Aster vs. TigerGraph

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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

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

A complete, distributed, parallel graph computing platform supporting web-scale data analytics in real-time

Primary database model

Relational DBMS

Graph DBMS

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

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

spark.apache.org/sql

www.tigergraph.com

Technical documentation

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

docs.tigergraph.com

Developer

Apache Software Foundation

Teradata

Initial release

2014

2005

2017

Current release

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

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

Scala

C++

Server operating systems

Linux
OS X
Windows

Linux

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

GSQL (TigerGraph Query Language)
Kafka
RESTful HTTP/JSON API

Supported programming languages

Java
Python
R
Scala

C
C#
C++
Java
Python
R

C++
Java

Server-side scripts

Stored procedures

R packages

yes

Triggers

Partitioning methods

Methods for storing different data on different nodes

yes, utilizing Spark Core

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

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

SQL Map-Reduce Framework

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Relationships in graphs

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.

User concepts

Access control

fine grained access rights according to SQL-standard

Role-based access control

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