DBMS > Hazelcast vs. Kinetica vs. Microsoft Access vs. Oracle NoSQL vs. Snowflake

System Properties Comparison Hazelcast vs. Kinetica vs. Microsoft Access vs. Oracle NoSQL vs. Snowflake

Editorial information provided by DB-Engines

Name

Description

A widely adopted in-memory data grid

Fully vectorized database across both GPUs and CPUs

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 multi-model, scalable, distributed NoSQL database, designed to provide highly reliable, flexible, and available data management across a configurable set of storage nodes

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

Primary database model

Key-value store

Relational DBMS

Document store
Key-value store
Relational DBMS

Relational DBMS

Secondary database models

Document store

JSON support with IMDG 3.12

Spatial DBMS
Time Series DBMS

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

Score	5.97
Rank	#57	Overall
	#6	Key-value stores

Score	0.64
Rank	#236	Overall
	#109	Relational DBMS

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	2.95
Rank	#100	Overall
	#17	Document stores
	#17	Key-value stores
	#50	Relational DBMS

Score	121.33
Rank	#9	Overall
	#6	Relational DBMS

Website

hazelcast.com

www.kinetica.com

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

www.oracle.com/database/nosql/technologies/nosql

www.snowflake.com

Technical documentation

hazelcast.org/imdg/docs

docs.kinetica.com

developer.microsoft.com/en-us/access

docs.oracle.com/en/database/other-databases/nosql-database/index.html

docs.snowflake.net/manuals/index.html

Developer

Hazelcast

Kinetica

Microsoft

Oracle

Snowflake Computing Inc.

Initial release

2008

2012

1992

2011

2014

Current release

5.3.6, November 2023

7.1, August 2021

1902 (16.0.11328.20222), March 2019

23.3, December 2023

License

Commercial or Open Source

Open Source

Apache Version 2; commercial licenses available

commercial

Bundled with Microsoft Office

Open Source

Proprietary for Enterprise Edition (Oracle Database EE license has Oracle NoSQL database EE covered: details)

commercial

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

Java

C, C++

C++

Java

Server operating systems

All OS with a Java VM

Linux

Windows

Not a real database server, but making use of DLLs

Linux
Solaris SPARC/x86

hosted

Data scheme

schema-free

yes

Support Fixed schema and Schema-less deployment with the ability to interoperate between them.

yes

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

Typing

predefined data types such as float or date

yes

optional

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

the object must implement a serialization strategy

yes

Secondary indexes

yes

SQL

Support of SQL

SQL-like query language

SQL-like DML and DDL statements

yes

but not compliant to any SQL standard

SQL-like DML and DDL statements

yes

APIs and other access methods

JCache
JPA
Memcached protocol
RESTful HTTP API

JDBC
ODBC
RESTful HTTP API

ADO.NET
DAO
ODBC
OLE DB

RESTful HTTP API

CLI Client
JDBC
ODBC

Supported programming languages

.Net
C#
C++
Clojure
Go
Java
JavaScript (Node.js)
Python
Scala

C++
Java
JavaScript (Node.js)
Python

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

C
C#
Go
Java
JavaScript (Node.js)
Python

JavaScript (Node.js)
Python

Server-side scripts

Stored procedures

yes

Event Listeners, Executor Services

user defined functions

yes

since Access 2010 using the ACE-engine

user defined functions

Triggers

yes

Events

yes

triggers when inserted values for one or more columns fall within a specified range

yes

since Access 2010 using the ACE-engine

similar concept for controling cloud resources

Partitioning methods

Methods for storing different data on different nodes

Sharding

none

Sharding

yes

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Replicated Map

Source-replica replication

none

Electable source-replica replication per shard. Support distributed global deployment with Multi-region table feature

yes

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency or Eventual Consistency selectable by user

Raft Consensus Algorithm

Immediate Consistency or Eventual Consistency depending on configuration

Eventual Consistency
Immediate Consistency

depending on configuration

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

one or two-phase-commit; repeatable reads; read commited

ACID

but no files for transaction logging

configurable

ACID within a storage node (=shard)

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

GPU vRAM or System RAM

yes

off heap cache

User concepts

Access control

Role-based access control

Access rights for users and roles on table level

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

Access rights for users and roles

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

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