DBMS > Amazon DynamoDB vs. Google Cloud Datastore vs. Kinetica vs. OpenMLDB vs. Snowflake

System Properties Comparison Amazon DynamoDB vs. Google Cloud Datastore vs. Kinetica vs. OpenMLDB vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Hosted, scalable database service by Amazon with the data stored in Amazons cloud

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

Fully vectorized database across both GPUs and CPUs

An open-source machine learning database that provides a feature platform for training and inference

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

Primary database model

Document store
Key-value store

Secondary database models

Spatial DBMS
Time Series DBMS

Relational DBMS

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

Score	74.45
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	0.66
Rank	#234	Overall
	#107	Relational DBMS

Score	0.10
Rank	#359	Overall
	#36	Time Series DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

aws.amazon.com/dynamodb

cloud.google.com/datastore

www.kinetica.com

openmldb.ai

www.snowflake.com

Technical documentation

docs.aws.amazon.com/dynamodb

cloud.google.com/datastore/docs

docs.kinetica.com

openmldb.ai/docs/zh/main

docs.snowflake.net/manuals/index.html

Developer

Amazon

Google

Kinetica

4 Paradigm Inc.

Snowflake Computing Inc.

Initial release

2012

2008

2012

2020

2014

Current release

7.1, August 2021

2024-2 February 2024

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

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++

C++, Java, Scala

Server operating systems

hosted

Linux

hosted

Data scheme

schema-free

yes

Fixed schema

yes

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

Typing

predefined data types such as float or date

yes

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

SQL-like query language (GQL)

SQL-like DML and DDL statements

yes

APIs and other access methods

RESTful HTTP API

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

JDBC
ODBC
RESTful HTTP API

JDBC
SQLAlchemy

CLI Client
JDBC
ODBC

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

C++
Java
JavaScript (Node.js)
Python

C++
Go
Java
Python
Scala

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

user defined functions

Triggers

yes

by integration with AWS Lambda

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

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Multi-source replication using Paxos

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

yes

using Google Cloud Dataflow

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

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

ACID across one or more tables within a single AWS account and region

ACID

Serializable Isolation within Transactions, Read Committed outside of Transactions

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

yes

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

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

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