DBMS > atoti vs. Google Cloud Datastore vs. Kinetica vs. Linter vs. Snowflake

System Properties Comparison atoti vs. Google Cloud Datastore vs. Kinetica vs. Linter vs. Snowflake

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

RDBMS for high security requirements

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

Primary database model

Object oriented DBMS

Document store

Relational DBMS

Secondary database models

Spatial DBMS
Time Series DBMS

Spatial DBMS

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

Score	0.56
Rank	#245	Overall
	#10	Object oriented DBMS

Score	4.47
Rank	#76	Overall
	#12	Document stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	0.09
Rank	#346	Overall
	#152	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

atoti.io

cloud.google.com/datastore

www.kinetica.com

linter.ru

www.snowflake.com

Technical documentation

docs.atoti.io

cloud.google.com/datastore/docs

docs.kinetica.com

docs.snowflake.net/manuals/index.html

Developer

ActiveViam

Google

Kinetica

relex.ru

Snowflake Computing Inc.

Initial release

2008

2012

1990

2014

Current release

7.1, August 2021

License

Commercial or Open Source

commercial

free versions 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

C, C++

C and C++

Server operating systems

hosted

Linux

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

hosted

Data scheme

schema-free

yes

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

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

APIs and other access methods

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

JDBC
ODBC
RESTful HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

CLI Client
JDBC
ODBC

Supported programming languages

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

C++
Java
JavaScript (Node.js)
Python

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

Python

using Google App Engine

user defined functions

yes

proprietary syntax with the possibility to convert from PL/SQL

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

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

Multi-source replication using Paxos

Source-replica replication

yes

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

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

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

Access rights for users and roles on table level

fine grained access rights according to SQL-standard

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

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.