DBMS > Alibaba Cloud AnalyticDB for PostgreSQL vs. Datomic vs. Google BigQuery vs. HugeGraph vs. Microsoft Access

System Properties Comparison Alibaba Cloud AnalyticDB for PostgreSQL vs. Datomic vs. Google BigQuery vs. HugeGraph vs. Microsoft Access

Editorial information provided by DB-Engines

Name

Description

An online MPP (Massively Parallel Processing) data warehousing service based on Greenplum

Datomic builds on immutable values, supports point-in-time queries and uses 3rd party systems for durability

Large scale data warehouse service with append-only tables

A fast-speed and highly-scalable Graph DBMS

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.)

Primary database model

Relational DBMS

Graph DBMS

Relational DBMS

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

Score	0.68
Rank	#231	Overall
	#105	Relational DBMS

Score	1.59
Rank	#150	Overall
	#69	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.13
Rank	#336	Overall
	#32	Graph DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Website

www.alibabacloud.com/product/hybriddb-postgresql

www.datomic.com

cloud.google.com/bigquery

github.com/hugegraph
hugegraph.apache.org

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

Technical documentation

www.alibabacloud.com/help/en/analyticdb-for-postgresql/latest/product-introduction-overview

docs.datomic.com

cloud.google.com/bigquery/docs

hugegraph.apache.org/docs

developer.microsoft.com/en-us/access

Developer

Alibaba / Pivotal Software Inc. / PostgreSQL Global Development Group

Cognitect

Google

Baidu

Microsoft

Initial release

2016

2012

2010

2018

1992

Current release

1.0.6735, June 2023

0.9

1902 (16.0.11328.20222), March 2019

License

Commercial or Open Source

commercial

limited edition free

commercial

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

Cloud-based only

Only available as a cloud service

yes

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

Java, Clojure

Java

C++

Server operating systems

hosted

All OS with a Java VM

hosted

Linux
macOS
Unix

Windows

Not a real database server, but making use of DLLs

Data scheme

yes

Typing

predefined data types such as float or date

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

also supports composite index and range index

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

APIs and other access methods

JDBC
ODBC

RESTful HTTP API

RESTful HTTP/JSON API

Java API
RESTful HTTP API
TinkerPop Gremlin

ADO.NET
DAO
ODBC
OLE DB

Supported programming languages

C
Java
Perl
Python
R

Clojure
Java

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Groovy
Java
Python

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

Server-side scripts

Stored procedures

yes

Transaction Functions

user defined functions

in JavaScript

asynchronous Gremlin script jobs

yes

since Access 2010 using the ACE-engine

Triggers

yes

By using transaction functions

yes

since Access 2010 using the ACE-engine

Partitioning methods

Methods for storing different data on different nodes

Sharding

Implicit feature of the cloud service

none

But extensive use of caching in the application peers

none

yes

depending on used storage backend, e.g. Cassandra and HBase

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

none

But extensive use of caching in the application peers

yes

depending on used storage backend, e.g. Cassandra and HBase

none

MapReduce

Offers an API for user-defined Map/Reduce methods

via hugegraph-spark

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

yes

edges in graph

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Since BigQuery is designed for querying data

ACID

but no files for transaction logging

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

using external storage systems (e.g. Cassandra, DynamoDB, PostgreSQL, Couchbase and others)

yes

but no files for transaction logging

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

yes

recommended only for testing and development

yes

User concepts

Access control

fine grained access rights according to SQL-standard

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

Users, roles and permissions

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

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