DBMS > atoti vs. Google Cloud Datastore vs. Kinetica vs. Snowflake vs. SQL.JS

System Properties Comparison atoti vs. Google Cloud Datastore vs. Kinetica vs. Snowflake vs. SQL.JS

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.

Automatically scaling NoSQL Database as a Service (DBaaS) on the Google Cloud Platform

Fully vectorized database across both GPUs and CPUs

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

Port of SQLite to JavaScript

Primary database model

Object oriented DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

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

Score	0.61
Rank	#243	Overall
	#10	Object oriented DBMS

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Score	0.63
Rank	#241	Overall
	#112	Relational DBMS

Website

atoti.io

cloud.google.com/datastore

www.kinetica.com

www.snowflake.com

sql.js.org

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

docs.kinetica.com

docs.snowflake.net/manuals/index.html

sql.js.org/documentation/index.html

Developer

ActiveViam

Google

Kinetica

Snowflake Computing Inc.

Alon Zakai

enhancements implemented by others

Initial release

2008

2012

2014

2012

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

free versions available

commercial

Open Source

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

C, C++

JavaScript

Server operating systems

hosted

Linux

hosted

server-less, requires a JavaScript environment (browser, Node.js)

Data scheme

schema-free

yes

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

yes

Typing

predefined data types such as float or date

yes, details here

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

Secondary indexes

yes

SQL

Support of SQL

Multidimensional Expressions (MDX)

SQL-like query language (GQL)

SQL-like DML and DDL statements

yes

SQL-92 is not fully supported

APIs and other access methods

gRPC (using protocol buffers) API
RESTful HTTP/JSON API

JDBC
ODBC
RESTful HTTP API

CLI Client
JDBC
ODBC

JavaScript API

Supported programming languages

.Net
Go
Java
JavaScript (Node.js)
PHP
Python
Ruby

C++
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

JavaScript

Server-side scripts

Stored procedures

Python

using Google App Engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

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

Sharding, horizontal partitioning

Sharding

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

Source-replica replication

yes

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency depending on type of query and configuration

Strong Consistency is default for entity lookups and queries within an Entity Group (but can instead be made eventually consistent). Other queries are always eventual consistent.

Immediate Consistency or Eventual Consistency depending on configuration

Immediate Consistency

none

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

Transaction concepts

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

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

ACID

Concurrency

Support for concurrent manipulation of data

yes, multi-version concurrency control (MVCC)

yes

Durability

Support for making data persistent

yes

except by serializing a db to a file

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

yes

User concepts

Access control

Access rights for users, groups and roles based on Google Cloud Identity and Access Management (IAM)

Access rights for users and roles on table level

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

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