DBMS > atoti vs. Citus vs. Google BigQuery vs. Snowflake

System Properties Comparison atoti vs. Citus vs. Google BigQuery vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

An in-memory DBMS combining transactional and analytical processing to handle the aggregation of ever-changing data.

Scalable hybrid operational and analytics RDBMS for big data use cases based on PostgreSQL

Large scale data warehouse service with append-only tables

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

Primary database model

Object oriented DBMS

Relational DBMS

Secondary database models

Document store

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	2.21
Rank	#118	Overall
	#56	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

atoti.io

www.citusdata.com

cloud.google.com/bigquery

www.snowflake.com

Technical documentation

docs.atoti.io

docs.citusdata.com

cloud.google.com/bigquery/docs

docs.snowflake.net/manuals/index.html

Developer

ActiveViam

Google

Snowflake Computing Inc.

Initial release

2010

2014

Current release

8.1, December 2018

License

Commercial or Open Source

commercial

free versions available

Open Source

AGPL, commercial license also available

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

Server operating systems

Linux

hosted

Data scheme

yes

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

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

specific XML type available, but no XML query functionality

yes

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

yes

standard, with numerous extensions

yes

APIs and other access methods

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

RESTful HTTP/JSON API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C
C++
Delphi
Java
JavaScript (Node.js)
Perl
PHP
Python
Tcl

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Python

user defined functions

realized in proprietary language PL/pgSQL or with common languages like Perl, Python, Tcl etc.

user defined functions

in JavaScript

user defined functions

Triggers

yes

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding, horizontal partitioning

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

other methods possible by using 3rd party extensions

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

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, multi-version concurrency control (MVCC)

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

Google Cloud Identity & Access Management (IAM)

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

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