DBMS > Google BigQuery vs. Kinetica vs. Snowflake vs. Teradata Aster

System Properties Comparison Google BigQuery vs. Kinetica vs. Snowflake vs. Teradata Aster

Please select another system to include it in the comparison.

Editorial information provided by DB-Engines

Name

Teradata Aster has been integrated into other Teradata systems and therefore will be removed from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

Fully vectorized database across both GPUs and CPUs

Cloud-based data warehousing service for structured and semi-structured data

Platform for big data analytics on multistructured data sources and types

Primary database model

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	0.69
Rank	#234	Overall
	#107	Relational DBMS

Score	123.20
Rank	#9	Overall
	#6	Relational DBMS

Website

cloud.google.com/bigquery

www.kinetica.com

www.snowflake.com

Technical documentation

cloud.google.com/bigquery/docs

docs.kinetica.com

docs.snowflake.net/manuals/index.html

Developer

Google

Kinetica

Snowflake Computing Inc.

Teradata

Initial release

2010

2012

2014

2005

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++

Server operating systems

hosted

Linux

hosted

Linux

Data scheme

yes

support of semi-structured data formats (JSON, XML, Avro)

Flexible Schema (defined schema, partial schema, schema free)

defined schema within the relational store; partial schema or schema free in the Aster File Store

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

in Aster File Store

Secondary indexes

yes

SQL

Support of SQL

yes

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

ADO.NET
JDBC
ODBC
OLE DB

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

C
C#
C++
Java
Python
R

Server-side scripts

Stored procedures

user defined functions

in JavaScript

user defined functions

R packages

Triggers

yes

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

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes

Dimension tables are replicated across all nodes in the cluster. The number of replicas for the file store can be configured.

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

SQL Map-Reduce Framework

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Immediate 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

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

Users with fine-grained authorization concept, user roles and pluggable authentication

fine grained access rights according to SQL-standard

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.