DBMS > Google Cloud Datastore vs. Google Cloud Spanner vs. Microsoft Access vs. SAP HANA

System Properties Comparison Google Cloud Datastore vs. Google Cloud Spanner vs. Microsoft Access vs. SAP HANA

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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 horizontally scalable, globally consistent, relational database service. It is the externalization of the core Google database that runs the biggest aspects of Google, like Ads and Google Play.

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.)

In-memory, column based data store. Available as appliance or cloud service

Primary database model

Document store

Relational DBMS

Secondary database models

Document store
Graph DBMS

with SAP Hana, Enterprise Edition
Spatial DBMS

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

Score	4.13
Rank	#71	Overall
	#12	Document stores

Score	2.69
Rank	#101	Overall
	#51	Relational DBMS

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Score	41.32
Rank	#23	Overall
	#16	Relational DBMS

Website

cloud.google.com/datastore

cloud.google.com/spanner

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

www.sap.com/products/hana.html

Technical documentation

cloud.google.com/datastore/docs

cloud.google.com/spanner/docs

developer.microsoft.com/en-us/access

help.sap.com/hana

Developer

Google

Microsoft

SAP

Initial release

2008

2017

1992

2010

Current release

1902 (16.0.11328.20222), March 2019

2.0 SPS07 (April 4, 2023), April 2023

License

Commercial or Open Source

commercial

Bundled with Microsoft Office

commercial

Cloud-based only

Only available as a cloud service

yes

also available as a cloud based service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Appliance or cloud-service

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language (GQL)

yes

Query statements complying to ANSI 2011

yes

but not compliant to any SQL standard

yes

APIs and other access methods

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

gRPC (using protocol buffers) API
JDBC

At present, JDBC supports read-only queries. No support for DDL or DML statements.
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

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

Go
Java
JavaScript (Node.js)
Python

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

Server-side scripts

Stored procedures

using Google App Engine

yes

since Access 2010 using the ACE-engine

SQLScript, R

Triggers

Callbacks using the Google Apps Engine

yes

since Access 2010 using the ACE-engine

yes

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

Multi-source replication with 3 replicas for regional instances.

none

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

using Google Cloud Dataflow

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

Foreign keys

Referential integrity

yes

via ReferenceProperties or Ancestor paths

yes

by using interleaved tables, this features focuses more on performance improvements than on referential integrity

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

Strict serializable isolation

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)

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

yes

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