DBMS > Google Cloud Datastore vs. Hive vs. OrigoDB vs. Snowflake

System Properties Comparison Google Cloud Datastore vs. Hive vs. OrigoDB vs. Snowflake

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Editorial information provided by DB-Engines

Name

Description

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

data warehouse software for querying and managing large distributed datasets, built on Hadoop

A fully ACID in-memory object graph database

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

Primary database model

Document store

Relational DBMS

Document store
Object oriented DBMS

Relational DBMS

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

Score	4.36
Rank	#72	Overall
	#12	Document stores

Score	59.76
Rank	#18	Overall
	#12	Relational DBMS

Score	0.06
Rank	#380	Overall
	#50	Document stores
	#18	Object oriented DBMS

Score	130.36
Rank	#8	Overall
	#5	Relational DBMS

Website

cloud.google.com/datastore

hive.apache.org

origodb.com

www.snowflake.com

Technical documentation

cloud.google.com/datastore/docs

cwiki.apache.org/confluence/display/Hive/Home

origodb.com/docs

docs.snowflake.net/manuals/index.html

Developer

Google

Apache Software Foundation

initially developed by Facebook

Robert Friberg et al

Snowflake Computing Inc.

Initial release

2008

2012

2009

under the name LiveDB

2014

Current release

3.1.3, April 2022

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

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

Java

Server operating systems

hosted

All OS with a Java VM

Linux
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

User defined using .NET types and collections

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.

can be achieved using .NET

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

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

JDBC
ODBC
Thrift

.NET Client API
HTTP API
LINQ

CLI Client
JDBC
ODBC

Supported programming languages

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

C++
Java
PHP
Python

.Net

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

user defined functions and integration of map-reduce

yes

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Domain Events

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

horizontal partitioning

client side managed; servers are not synchronized

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

selectable replication factor

Source-replica replication

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

query execution via MapReduce

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.

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

depending on model

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

Durability

Support for making data persistent

yes

Write ahead log

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

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

Access rights for users, groups and roles

Role based authorization

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

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