DBMS > Amazon Redshift vs. GBase vs. Spark SQL vs. TigerGraph

System Properties Comparison Amazon Redshift vs. GBase vs. Spark SQL vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

Widely used RDBMS in China, including analytical, transactional, distributed transactional, and cloud-native data warehousing.

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

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	17.94
Rank	#34	Overall
	#21	Relational DBMS

Score	1.07
Rank	#185	Overall
	#86	Relational DBMS

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

aws.amazon.com/redshift

www.gbase.cn

spark.apache.org/sql

www.tigergraph.com

Technical documentation

docs.aws.amazon.com/redshift

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

docs.tigergraph.com

Developer

Amazon (based on PostgreSQL)

General Data Technology Co., Ltd.

Apache Software Foundation

Initial release

2012

2004

2014

2017

Current release

GBase 8a, GBase 8s, GBase 8c

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

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

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Implementation language

C, Java, Python

Scala

C++

Server operating systems

hosted

Linux

Linux
OS X
Windows

Linux

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

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

Standard with numerous extensions

SQL-like DML and DDL statements

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

ADO.NET
C API
JDBC
ODBC

JDBC
ODBC

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

Supported programming languages

All languages supporting JDBC/ODBC

Java
Python
R
Scala

C++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning (by range, list and hash) and vertical partitioning

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

informational only, not enforced by the system

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.

yes

User concepts

Access control

fine grained access rights according to SQL-standard

yes

Role-based access control

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