DBMS > Apache Pinot vs. Microsoft Access vs. Newts vs. Riak TS vs. Snowflake

System Properties Comparison Apache Pinot vs. Microsoft Access vs. Newts vs. Riak TS vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

Realtime distributed OLAP datastore, designed to answer OLAP queries with low latency

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.)

Time Series DBMS based on Cassandra

Riak TS is a distributed NoSQL database optimized for time series data and based on Riak KV

Cloud-based data warehousing service for structured and semi-structured data

Primary database model

Relational DBMS

Time Series DBMS

Relational DBMS

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

Score	0.40
Rank	#270	Overall
	#125	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.20
Rank	#319	Overall
	#27	Time Series DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

pinot.apache.org

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

opennms.github.io/newts

www.snowflake.com

Technical documentation

docs.pinot.apache.org

developer.microsoft.com/en-us/access

github.com/OpenNMS/newts/wiki

www.tiot.jp/riak-docs/riak/ts/latest

docs.snowflake.net/manuals/index.html

Developer

Apache Software Foundation and contributors

Microsoft

OpenNMS Group

Open Source, formerly Basho Technologies

Snowflake Computing Inc.

Initial release

2015

1992

2014

2015

2014

Current release

1.0.0, September 2023

1902 (16.0.11328.20222), March 2019

3.0.0, September 2022

License

Commercial or Open Source

Open Source

Apache Version 2.0

commercial

Bundled with Microsoft Office

Open Source

Apache 2.0

Open Source

commercial

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

C++

Java

Erlang

Server operating systems

All OS with a Java JDK11 or higher

Windows

Not a real database server, but making use of DLLs

Linux
OS X
Windows

Linux
OS X

hosted

Data scheme

yes

schema-free

yes

support of semi-structured data formats (JSON, XML, Avro)

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

restricted

SQL

Support of SQL

SQL-like query language

yes

but not compliant to any SQL standard

yes, limited

yes

APIs and other access methods

JDBC

ADO.NET
DAO
ODBC
OLE DB

HTTP REST
Java API

HTTP API
Native Erlang Interface

CLI Client
JDBC
ODBC

Supported programming languages

Go
Java
Python

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

Java

unofficial client library
C#
C++

unofficial client library
Clojure

unofficial client library
Dart

unofficial client library
Erlang
Go

unofficial client library
Groovy

unofficial client library
Haskell

unofficial client library
Java
JavaScript

unofficial client library
Lisp

unofficial client library
Perl

unofficial client library
PHP
Python
Ruby
Scala

unofficial client library
Smalltalk

unofficial client library

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

since Access 2010 using the ACE-engine

Erlang

user defined functions

Triggers

yes

since Access 2010 using the ACE-engine

yes

pre-commit hooks and post-commit hooks

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

horizontal partitioning

none

Sharding

based on Cassandra

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

none

selectable replication factor

based on Cassandra

selectable replication factor

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Eventual Consistency

based on Cassandra
Immediate Consistency

based on Cassandra

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

yes

links between datasets can be stored

yes

Transaction concepts

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

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

In-memory capabilities

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

User concepts

Access control

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

Users with fine-grained authorization concept, user roles and pluggable authentication

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