DBMS > Google Cloud Datastore vs. Hazelcast vs. Microsoft Access vs. Snowflake

System Properties Comparison Google Cloud Datastore vs. Hazelcast vs. Microsoft Access 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

A widely adopted in-memory data grid

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

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

Primary database model

Document store

Key-value store

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

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Trend Chart

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	5.72
Rank	#59	Overall
	#6	Key-value stores

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Score	133.72
Rank	#7	Overall
	#5	Relational DBMS

Website

cloud.google.com/datastore

hazelcast.com

www.microsoft.com/en-us/microsoft-365/access

www.snowflake.com

Technical documentation

cloud.google.com/datastore/docs

hazelcast.org/imdg/docs

developer.microsoft.com/en-us/access

docs.snowflake.net/manuals/index.html

Developer

Google

Hazelcast

Microsoft

Snowflake Computing Inc.

Initial release

2008

1992

2014

Current release

5.3.6, November 2023

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

Open Source

Apache Version 2; commercial licenses available

commercial

Bundled with Microsoft Office

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++

Server operating systems

hosted

All OS with a Java VM

Windows

Not a real database server, but making use of DLLs

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

SQL-like query language

yes

but not compliant to any SQL standard

yes

APIs and other access methods

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

JCache
JPA
Memcached protocol
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

CLI Client
JDBC
ODBC

Supported programming languages

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

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

using Google App Engine

yes

Event Listeners, Executor Services

yes

since Access 2010 using the ACE-engine

user defined functions

Triggers

Callbacks using the Google Apps Engine

yes

Events

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication using Paxos

yes

Replicated Map

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

yes

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 selectable by user

Raft Consensus Algorithm

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

one or two-phase-commit; repeatable reads; read commited

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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)

Role-based access control

a simple user-level security was built in till version Access 2003

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

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