DBMS > Amazon Neptune vs. Couchbase vs. Microsoft Access vs. Realm vs. Spark SQL

System Properties Comparison Amazon Neptune vs. Couchbase vs. Microsoft Access vs. Realm vs. Spark SQL

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

A distributed document store with integrated cache, a powerful search engine, in-built operational and analytical capabilities, and an embedded mobile database

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 DBMS built for use on mobile devices that’s a fast, easy to use alternative to SQLite and Core Data

Spark SQL is a component on top of 'Spark Core' for structured data processing

Primary database model

Secondary database models

Key-value store

originating from the former Membase product and supporting the Memcached protocol
Spatial DBMS

using the Geocouch extension
Search engine
Time Series DBMS
Vector DBMS

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

Score	2.20
Rank	#119	Overall
	#9	Graph DBMS
	#5	RDF stores

Score	17.30
Rank	#36	Overall
	#5	Document stores

Score	104.92
Rank	#11	Overall
	#8	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	18.96
Rank	#33	Overall
	#20	Relational DBMS

Website

aws.amazon.com/neptune

www.couchbase.com

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

realm.io

spark.apache.org/sql

Technical documentation

aws.amazon.com/neptune/developer-resources

docs.couchbase.com

developer.microsoft.com/en-us/access

realm.io/docs

spark.apache.org/docs/latest/sql-programming-guide.html

Developer

Amazon

Couchbase, Inc.

Microsoft

Realm, acquired by MongoDB in May 2019

Apache Software Foundation

Initial release

2017

2011

1992

2014

Current release

Server: 7.2, June 2023; Mobile: 3.1, March 2022; Couchbase Capella (DBaaS), June 2023

1902 (16.0.11328.20222), March 2019

3.5.0 ( 2.13), September 2023

License

Commercial or Open Source

commercial

Open Source

Business Source License (BSL 1.1); Commercial licenses also available

commercial

Bundled with Microsoft Office

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, C++, Go and Erlang

C++

Scala

Server operating systems

hosted

Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

Android
Backend: server-less
iOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

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.

Secondary indexes

yes

SQL

Support of SQL

SQL++, extends ANSI SQL to JSON for operational, transactional, and analytic use cases

yes

but not compliant to any SQL standard

SQL-like DML and DDL statements

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

CLI Client
HTTP REST
Kafka Connector
Native language bindings for CRUD, Query, Search and Analytics APIs
Spark Connector
Spring Data

ADO.NET
DAO
ODBC
OLE DB

JDBC
ODBC

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
C
Go
Java
JavaScript

Node.js
Kotlin
PHP
Python
Ruby
Scala

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

.Net
Java

with Android only
Objective-C
React Native
Swift

Java
Python
R
Scala

Server-side scripts

Stored procedures

Functions and timers in JavaScript and UDFs in Java, Python, SQL++

yes

since Access 2010 using the ACE-engine

runs within the applications so server-side scripts are unnecessary

Triggers

yes

via the TAP protocol

yes

since Access 2010 using the ACE-engine

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Automatic Sharding

none

yes, utilizing Spark Core

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-availability zones high availability, asynchronous replication for up to 15 read replicas within a single region. Global database clusters consists of a primary write DB cluster in one region, and up to five secondary read DB clusters in different regions. Each secondary region can have up to 16 reader instances.

Multi-source replication

including cross data center replication
Source-replica replication

none

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency

selectable on a per-operation basis

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

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

with encyption-at-rest

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

Ephemeral buckets

yes

In-Memory realm

User concepts

Access control

Access rights for users and roles can be defined via the AWS Identity and Access Management (IAM)

User and Administrator separation with password-based and LDAP integrated Authentication. Role-base access control.

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

yes

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