DBMS > Amazon DynamoDB vs. Microsoft Access vs. Quasardb vs. TimescaleDB

System Properties Comparison Amazon DynamoDB vs. Microsoft Access vs. Quasardb vs. TimescaleDB

Please select another system to include it in the comparison.

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.)

Distributed, high-performance timeseries database

A time series DBMS optimized for fast ingest and complex queries, based on PostgreSQL

Primary database model

Document store
Key-value store

Relational DBMS

Time Series DBMS

Secondary database models

Relational DBMS

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

Score	74.07
Rank	#17	Overall
	#3	Document stores
	#2	Key-value stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	4.64
Rank	#71	Overall
	#4	Time Series DBMS

Website

aws.amazon.com/dynamodb

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

quasar.ai

www.timescale.com

Technical documentation

docs.aws.amazon.com/dynamodb

developer.microsoft.com/en-us/access

doc.quasar.ai/master

docs.timescale.com

Developer

Amazon

Microsoft

quasardb

Timescale

Initial release

2012

1992

2009

2017

Current release

1902 (16.0.11328.20222), March 2019

3.14.1, January 2024

2.13.0, November 2023

License

Commercial or Open Source

commercial

free tier for a limited amount of database operations

commercial

Bundled with Microsoft Office

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

Open Source

Apache 2.0

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++

Server operating systems

hosted

Windows

Not a real database server, but making use of DLLs

BSD
Linux
OS X
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

integer and binary

numerics, strings, booleans, arrays, JSON blobs, geospatial dimensions, currencies, binary data, other complex data types

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

with tags

yes

SQL

Support of SQL

yes

but not compliant to any SQL standard

SQL-like query language

yes

full PostgreSQL SQL syntax

APIs and other access methods

RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

HTTP API

ADO.NET
JDBC
native C library
ODBC
streaming API for large objects

Supported programming languages

.Net
ColdFusion
Erlang
Groovy
Java
JavaScript
Perl
PHP
Python
Ruby

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

.Net
C
C++
Delphi
Java

JDBC
JavaScript
Perl
PHP
Python
R
Ruby
Scheme
Tcl

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

user defined functions, PL/pgSQL, PL/Tcl, PL/Perl, PL/Python, PL/Java, PL/PHP, PL/R, PL/Ruby, PL/Scheme, PL/Unix shell

Triggers

yes

by integration with AWS Lambda

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

consistent hashing

yes, across time and space (hash partitioning) attributes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

none

Source-replica replication with selectable replication factor

Source-replica replication with hot standby and reads on replicas

MapReduce

Offers an API for user-defined Map/Reduce methods

may be implemented via Amazon Elastic MapReduce (Amazon EMR)

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Immediate Consistency

can be specified for read operations

Immediate Consistency

Foreign keys

Referential integrity

yes

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

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

by using LevelDB

yes

In-memory capabilities

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

yes

Transient mode

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

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

More information provided by the system vendor

We invite representatives of system vendors to contact us for updating and extending the system information,
and for displaying vendor-provided information such as key customers, competitive advantages and market metrics.