DBMS > Amazon DynamoDB vs. Microsoft Access vs. QuestDB vs. Stardog

System Properties Comparison Amazon DynamoDB vs. Microsoft Access vs. QuestDB vs. Stardog

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Editorial information provided by DB-Engines

Name

Description

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

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.)

A high performance open source SQL database for time series data

Enterprise Knowledge Graph platform and graph DBMS with high availability, high performance reasoning, and virtualization

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Graph DBMS
RDF store

Secondary database models

Relational DBMS

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

Score	70.06
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Score	2.81
Rank	#98	Overall
	#7	Time Series DBMS

Score	1.93
Rank	#121	Overall
	#10	Graph DBMS
	#6	RDF stores

Website

aws.amazon.com/dynamodb

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

questdb.io

www.stardog.com

Technical documentation

docs.aws.amazon.com/dynamodb

developer.microsoft.com/en-us/access

questdb.io/docs

docs.stardog.com

Developer

Amazon

Microsoft

QuestDB Technology Inc

Stardog-Union

Initial release

2012

1992

2014

2010

Current release

1902 (16.0.11328.20222), March 2019

7.3.0, May 2020

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Bundled with Microsoft Office

Open Source

Apache 2.0

commercial

60-day fully-featured trial license; 1-year fully-featured non-commercial use license for academics/students

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++

Java (Zero-GC), C++, Rust

Java

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free via InfluxDB Line Protocol

schema-free and OWL/RDFS-schema support

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.

Import/export of XML data possible

Secondary indexes

yes

supports real-time indexing in full-text and geospatial

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL with time-series extensions

Yes, compatible with all major SQL variants through dedicated BI/SQL Server

APIs and other access methods

RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

HTTP REST
InfluxDB Line Protocol (TCP/UDP)
JDBC
PostgreSQL wire protocol

GraphQL query language
HTTP API
Jena RDF API
OWL
RDF4J API
Sesame REST HTTP Protocol
SNARL
SPARQL
Spring Data
Stardog Studio
TinkerPop 3

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

PostgreSQL driver
C++
Go
Java
JavaScript (Node.js)
Python
Rust

over HTTP

.Net
Clojure
Groovy
Java
JavaScript
Python
Ruby

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

user defined functions and aggregates, HTTP Server extensions in Java

Triggers

yes

by integration with AWS Lambda

yes

since Access 2010 using the ACE-engine

yes

via event handlers

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

horizontal partitioning (by timestamps)

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication with eventual consistency

Multi-source replication in HA-Cluster

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Immediate Consistency in HA-Cluster

Foreign keys

Referential integrity

yes

relationships in graphs

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

but no files for transaction logging

ACID for single-table writes

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

through memory mapped files

yes

User concepts

Access control

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

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

Access rights for users and roles

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