DBMS > Google BigQuery vs. Graph Engine vs. Hive vs. Kinetica

System Properties Comparison Google BigQuery vs. Graph Engine vs. Hive vs. Kinetica

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

A distributed in-memory data processing engine, underpinned by a strongly-typed RAM store and a general distributed computation engine

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

Fully vectorized database across both GPUs and CPUs

Primary database model

Relational DBMS

Graph DBMS
Key-value store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#232	Overall
	#21	Graph DBMS
	#34	Key-value stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Website

cloud.google.com/bigquery

www.graphengine.io

hive.apache.org

www.kinetica.com

Technical documentation

cloud.google.com/bigquery/docs

www.graphengine.io/docs/manual

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

docs.kinetica.com

Developer

Google

Microsoft

Apache Software Foundation

initially developed by Facebook

Kinetica

Initial release

2010

2012

Current release

3.1.3, April 2022

7.1, August 2021

License

Commercial or Open Source

commercial

Open Source

MIT License

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

.NET and C

Java

C, C++

Server operating systems

hosted

.NET

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

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

APIs and other access methods

RESTful HTTP/JSON API

RESTful HTTP API

JDBC
ODBC
Thrift

JDBC
ODBC
RESTful HTTP API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
F#
Visual Basic

C++
Java
PHP
Python

C++
Java
JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

user defined functions and integration of map-reduce

user defined functions

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

horizontal partitioning

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

query execution via MapReduce

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

Transaction concepts

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

Since BigQuery is designed for querying data

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

optional: either by committing a write-ahead log (WAL) to the local persistent storage or by dumping the memory to a persistent storage

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, groups and roles

Access rights for users and roles on table level

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