DBMS > Amazon Redshift vs. EXASOL vs. Google BigQuery vs. Hive vs. TigerGraph

System Properties Comparison Amazon Redshift vs. EXASOL vs. Google BigQuery vs. Hive vs. TigerGraph

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service for use with business intelligence tools

High-performance, in-memory, MPP database specifically designed for in-memory analytics.

Large scale data warehouse service with append-only tables

data warehouse software for querying and managing large distributed datasets, built on Hadoop

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

Primary database model

Relational DBMS

Graph DBMS

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Trend Chart

Score	16.88
Rank	#35	Overall
	#22	Relational DBMS

Score	1.76
Rank	#139	Overall
	#62	Relational DBMS

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	1.80
Rank	#138	Overall
	#13	Graph DBMS

Website

aws.amazon.com/redshift

www.exasol.com

cloud.google.com/bigquery

hive.apache.org

www.tigergraph.com

Technical documentation

docs.aws.amazon.com/redshift

www.exasol.com/resources

cloud.google.com/bigquery/docs

cwiki.apache.org/confluence/display/Hive/Home

docs.tigergraph.com

Developer

Amazon (based on PostgreSQL)

Exasol

Google

Apache Software Foundation

initially developed by Facebook

Initial release

2012

2000

2010

2012

2017

Current release

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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

Java

C++

Server operating systems

hosted

All OS with a Java VM

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.

Secondary indexes

restricted

yes

SQL

Support of SQL

yes

does not fully support an SQL-standard

yes

SQL-like DML and DDL statements

SQL-like query language (GSQL)

APIs and other access methods

JDBC
ODBC

.Net
JDBC
ODBC
WebSocket

RESTful HTTP/JSON API

JDBC
ODBC
Thrift

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

Supported programming languages

All languages supporting JDBC/ODBC

Java
Lua
Python
R

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
PHP
Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

in Python

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

yes

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

selectable replication factor

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Hadoop integration

yes

query execution via MapReduce

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

Since BigQuery is designed for querying 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

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

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles

Role-based access control

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