DBMS > Apache IoTDB vs. FeatureBase vs. Microsoft Access vs. Microsoft SQL Server vs. Yaacomo

System Properties Comparison Apache IoTDB vs. FeatureBase vs. Microsoft Access vs. Microsoft SQL Server vs. Yaacomo

Editorial information provided by DB-Engines

Name

Yaacomo seems to be discontinued and is removed from the DB-Engines ranking

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

Real-time database platform that powers real-time analytics and machine learning applications by simultaneously executing low-latency, high-throughput, and highly concurrent workloads.

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.)

Microsofts flagship relational DBMS

OpenCL based in-memory RDBMS, designed for efficiently utilizing the hardware via parallel computing

Primary database model

Time Series DBMS

Relational DBMS

Secondary database models

Document store
Graph DBMS
Spatial DBMS

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

Score	1.18
Rank	#173	Overall
	#15	Time Series DBMS

Score	0.22
Rank	#309	Overall
	#139	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	824.29
Rank	#3	Overall
	#3	Relational DBMS

Website

iotdb.apache.org

www.featurebase.com

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

www.microsoft.com/en-us/sql-server

yaacomo.com

Technical documentation

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

docs.featurebase.com

developer.microsoft.com/en-us/access

learn.microsoft.com/en-US/sql/sql-server

Developer

Apache Software Foundation

Molecula and Pilosa Open Source Contributors

Microsoft

Q2WEB GmbH

Initial release

2018

2017

1992

1989

2009

Current release

1.1.0, April 2023

2022, May 2022

1902 (16.0.11328.20222), March 2019

SQL Server 2022, November 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

commercial

restricted free version is available

commercial

Cloud-based only

Only available as a cloud service

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Implementation language

Java

C++

Server operating systems

All OS with a Java VM (>= 1.8)

Linux
macOS

Windows

Not a real database server, but making use of DLLs

Linux
Windows

Android
Linux
Windows

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

SQL

Support of SQL

SQL-like query language

SQL queries

yes

but not compliant to any SQL standard

yes

APIs and other access methods

JDBC
Native API

gRPC
JDBC
Kafka Connector
ODBC

ADO.NET
DAO
ODBC
OLE DB

ADO.NET
JDBC
ODBC
OLE DB
Tabular Data Stream (TDS)

JDBC
ODBC

Supported programming languages

C
C#
C++
Go
Java
Python
Scala

Java
Python

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

C#
C++
Delphi
Go
Java
JavaScript (Node.js)
PHP
Python
R
Ruby
Visual Basic

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

Transact SQL, .NET languages, R, Python and (with SQL Server 2019) Java

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)

Sharding

none

tables can be distributed across several files (horizontal partitioning); sharding through federation

horizontal partitioning

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

yes

none

yes, but depending on the SQL-Server Edition

Source-replica replication

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

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

yes

ACID

but no files for transaction logging

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, using Linux fsync

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

yes

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

fine grained access rights according to SQL-standard

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