DBMS > Apache IoTDB vs. Microsoft Access vs. SQLite vs. Trino

System Properties Comparison Apache IoTDB vs. Microsoft Access vs. SQLite vs. Trino

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Editorial information provided by DB-Engines

Name

Description

An IoT native database with high performance for data management and analysis, deployable on the edge and the cloud and integrated with Hadoop, Spark and Flink

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.)

Widely used embeddable, in-process RDBMS

Fast distributed SQL query engine for big data analytics. Forked from Presto and originally named PrestoSQL

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Key-value store
Spatial DBMS
Search engine
Time Series DBMS
Wide column store

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

Score	1.31
Rank	#159	Overall
	#14	Time Series DBMS

Score	93.76
Rank	#12	Overall
	#8	Relational DBMS

Score	103.35
Rank	#10	Overall
	#7	Relational DBMS

Score	5.28
Rank	#61	Overall
	#35	Relational DBMS

Website

iotdb.apache.org

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

www.sqlite.org

trino.io

Technical documentation

iotdb.apache.org/UserGuide/Master/QuickStart/QuickStart.html

developer.microsoft.com/en-us/access

www.sqlite.org/docs.html

trino.io/broadcast
trino.io/docs/current

Developer

Apache Software Foundation

Microsoft

Dwayne Richard Hipp

Trino Software Foundation

Initial release

2018

1992

2000

2012

2020 rebranded from PrestoSQL

Current release

1.1.0, April 2023

1902 (16.0.11328.20222), March 2019

3.46.1 (13 August 2024), August 2024

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

Open Source

Public Domain

Open Source

Apache Version 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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

Implementation language

Java

C++

Java

Server operating systems

All OS with a Java VM (>= 1.8)

Windows

Not a real database server, but making use of DLLs

server-less

Linux
macOS

for devlopment

Data scheme

yes

dynamic column types

yes

Typing

predefined data types such as float or date

yes

not rigid because of 'dynamic typing' concept.

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

depending on connected data-source

SQL

Support of SQL

SQL-like query language

yes

but not compliant to any SQL standard

yes

SQL-92 is not fully supported

yes

APIs and other access methods

JDBC
Native API

ADO.NET
DAO
ODBC
OLE DB

ADO.NET

inofficial driver
JDBC

inofficial driver
ODBC

inofficial driver

JDBC
RESTful HTTP API
Trino CLI

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

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

Actionscript
Ada
Basic
C
C#
C++
D
Delphi
Forth
Fortran
Haskell
Java
JavaScript
Lisp
Lua
MatLab
Objective-C
OCaml
Perl
PHP
PL/SQL
Python
R
Ruby
Scala
Scheme
Smalltalk
Tcl

Go
Java
JavaScript (Node.js)
Python
R
Ruby

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

yes, depending on connected data-source

Triggers

yes

since Access 2010 using the ACE-engine

yes

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning (by time range) + vertical partitioning (by deviceId)

none

depending on connected data-source

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication methods; using Raft/IoTConsensus algorithm to ensure strong/eventual data consistency among multiple replicas

none

depending on connected data-source

MapReduce

Offers an API for user-defined Map/Reduce methods

Integration with Hadoop and Spark

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency
Strong Consistency with Raft

depending on connected data-source

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

but no files for transaction logging

ACID

depending on connected data-source

Concurrency

Support for concurrent manipulation of data

yes

via file-system locks

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

yes

depending on connected data-source

In-memory capabilities

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

yes

User concepts

Access control

yes

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

SQL standard access control

More information provided by the system vendor

News

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17 September 2024

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12 September 2024

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22 August 2024

63: Querying with JS
1 August 2024

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11 July 2024

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