DBMS > Google BigQuery vs. Kinetica vs. TigerGraph

System Properties Comparison Google BigQuery vs. Kinetica vs. TigerGraph

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

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

Primary database model

Relational DBMS

Graph DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	1.83
Rank	#139	Overall
	#13	Graph DBMS

Website

cloud.google.com/bigquery

www.kinetica.com

www.tigergraph.com

Technical documentation

cloud.google.com/bigquery/docs

docs.kinetica.com

docs.tigergraph.com

Developer

Google

Kinetica

Initial release

2010

2012

2017

Current release

7.1, August 2021

License

Commercial or Open Source

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

C, C++

C++

Server operating systems

hosted

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

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

SQL-like query language (GSQL)

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

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

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

C++
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

yes

Triggers

yes

triggers when inserted values for one or more columns fall within a specified range

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

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

Relationships in graphs

Transaction concepts

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

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

GPU vRAM or System RAM

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users and roles on table level

Role-based access control

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