DBMS > Amazon Neptune vs. Apache Drill vs. Google BigQuery vs. Realm vs. SiteWhere

System Properties Comparison Amazon Neptune vs. Apache Drill vs. Google BigQuery vs. Realm vs. SiteWhere

Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Schema-free SQL Query Engine for Hadoop, NoSQL and Cloud Storage

Large scale data warehouse service with append-only tables

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

M2M integration platform for persisting/querying time series data

Primary database model

Graph DBMS
RDF store

Document store
Relational DBMS

Relational DBMS

Document store

Time Series 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	1.95
Rank	#127	Overall
	#23	Document stores
	#60	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	7.60
Rank	#52	Overall
	#9	Document stores

Score	0.06
Rank	#356	Overall
	#35	Time Series DBMS

Website

aws.amazon.com/neptune

drill.apache.org

cloud.google.com/bigquery

realm.io

github.com/sitewhere/sitewhere

Technical documentation

aws.amazon.com/neptune/developer-resources

drill.apache.org/docs

cloud.google.com/bigquery/docs

realm.io/docs

sitewhere1.sitewhere.io/index.html

Developer

Amazon

Apache Software Foundation

Google

Realm, acquired by MongoDB in May 2019

SiteWhere

Initial release

2017

2012

2010

2014

2010

Current release

1.20.3, January 2023

License

Commercial or Open Source

commercial

Open Source

Apache Version 2

commercial

Open Source

Common Public Attribution License Version 1.0

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

Server operating systems

hosted

Linux
OS X
Windows

hosted

Android
Backend: server-less
iOS
Windows

Linux
OS X
Windows

Data scheme

schema-free

yes

predefined scheme

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 SELECT statement is SQL:2003 compliant

yes

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

JDBC
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

HTTP REST

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

C++

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
Java

with Android only
Objective-C
React Native
Swift

Server-side scripts

Stored procedures

user defined functions

in JavaScript

runs within the applications so server-side scripts are unnecessary

Triggers

yes

Change Listeners

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Sharding

based on HBase

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.

none

selectable replication factor

based on HBase

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

none

Immediate Consistency

Foreign keys

Referential integrity

yes

Relationships in graphs

Transaction concepts

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

ACID

Since BigQuery is designed for querying data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

with encyption-at-rest

Depending on the underlying data source

yes

In-memory capabilities

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

Depending on the underlying data source

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)

Depending on the underlying data source

Access privileges (owner, writer, reader) on dataset, table or view level

Google Cloud Identity & Access Management (IAM)

yes

Users with fine-grained authorization concept

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