DB-EnginesCrateDB bannerEnglish
Deutsch
Knowledge Base of Relational and NoSQL Database Management Systemsprovided by solid IT

DBMS > Google BigQuery vs. Kinetica

System Properties Comparison Google BigQuery vs. Kinetica

Please select another system to include it in the comparison.

Our visitors often compare Google BigQuery and Kinetica with Vertica, MongoDB and Elasticsearch.

Editorial information provided by DB-Engines
NameGoogle BigQuery  Xexclude from comparisonKinetica  Xexclude from comparison
DescriptionLarge scale data warehouse service with append-only tablesGPU-accelerated database for real-time analysis of large and streaming datasets
Primary database modelRelational DBMSRelational DBMS
DB-Engines Ranking infomeasures the popularity of database management systemsranking trend
Trend Chart
Score24.47
Rank#28  Overall
#16  Relational DBMS
Score0.56
Rank#199  Overall
#98  Relational DBMS
Websitecloud.google.com/­bigquerywww.kinetica.com
Technical documentationcloud.google.com/­bigquery/­docswww.kinetica.com/­docs
DeveloperGoogleKinetica
Initial release20102012
Current release6.0
License infoCommercial or Open Sourcecommercialcommercial
Cloud-based only infoOnly available as a cloud serviceyesno
DBaaS offerings (sponsored links) infoDatabase as a Service

Providers of DBaaS offerings, please contact us to be listed.
Implementation languageC, C++
Server operating systemshostedLinux
Data schemeyesyes
Typing infopredefined data types such as float or dateyesyes
XML support infoSome form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.nono
Secondary indexesnoyes
SQL infoSupport of SQLyesSQL-like DML and DDL statements
APIs and other access methodsRESTful HTTP/JSON APIRESTful HTTP API
JDBC
ODBC
Supported programming languages.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby
C++
Java
JavaScript (Node.js)
Python
Server-side scripts infoStored proceduresuser defined functions infoin JavaScriptuser defined functions
Triggersnoyes infotriggers when inserted values for one or more columns fall within a specified range
Partitioning methods infoMethods for storing different data on different nodesnoneSharding
Replication methods infoMethods for redundantly storing data on multiple nodesMaster-slave replication
MapReduce infoOffers an API for user-defined Map/Reduce methodsnono
Consistency concepts infoMethods to ensure consistency in a distributed systemImmediate ConsistencyImmediate Consistency or Eventual Consistency depending on configuration
Foreign keys infoReferential integritynoyes
Transaction concepts infoSupport to ensure data integrity after non-atomic manipulations of datano infoSince BigQuery is designed for querying datano
Concurrency infoSupport for concurrent manipulation of datayesyes
Durability infoSupport for making data persistentyesyes
In-memory capabilities infoIs there an option to define some or all structures to be held in-memory only.noyes infoGPU vRAM or System RAM
User concepts infoAccess controlAccess privileges (owner, writer, reader) for whole datasets, not for individual tables infoGoogle Cloud Identity & Access Management (IAM)Access rights for users and roles on table level

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.

Related products and services
3rd partiesCData: Connect to Big Data & NoSQL through standard Drivers.
» more

We invite representatives of vendors of related products to contact us for presenting information about their offerings here.

More resources
Google BigQueryKinetica
DB-Engines blog posts

The popularity of cloud-based DBMSs has increased tenfold in four years
7 February 2017, Matthias Gelbmann

Increased popularity for consuming DBMS services out of the cloud
2 October 2015, Paul Andlinger

show all

Recent citations in the news

Acquisitions to Fuel Programmable Analytics
11 June 2019, DevOps.com

Comparing Redshift and BigQuery in various terms
13 December 2018, Analytics India Magazine

Google Cloud Platform breaks through with big enterprises, signs up Disney and others
23 March 2016, ZDNet

provided by Google News

Smart Vending Machines Deliver On Data
29 July 2019, Forbes

GPU Database Market SWOT Analysis including key players Kinetica, Omnisci, Sqream, Neo4j, Nvidia, Brytlyt, Jedox, Blazegraph, Blazingdb
26 July 2019, PortNews24

GPU Database Market Size, Status and Forecast 2019-2025 | Omnisci, Sqream, Kinetica
24 July 2019, Market Analyst

GPU Database Market SWOT Analysis by Key Futuristic Trends from 2019-2027 | Brytlyt, Fuzzy Logix, Jedox AG, Kinetica DB, Neo4j, OmniSci
8 August 2019, Rise Media

Kinetica Delivers GPU-Accelerated Analytics Engine to SoftBank
1 November 2018, Business Wire

provided by Google News

Job opportunities

Data Science Intern
Intersection Capital, Santa Monica, CA

jobs by Indeed




Share this page

Featured Products

Couchbase logo

SQL + JSON + NoSQL.
Power, flexibility & scale.
All open source.
Get started now.

Redis logo

Start now with Redis Cloud
Secure, highly available Redis as a serverless, hosted, fully managed cloud service.
Sign up here.

Neo4j logo

Get your free copy of the new O'Reilly book Graph Algorithms with 20+ examples for
machine learning, graph analytics and more.

AllegroGraph logo

Graph Database Leader for AI Knowledge Graph Applications - The Most Secure Graph Database Available.
Free Download

Present your product here