DBMS > Amazon Neptune vs. Google BigQuery vs. NSDb vs. SurrealDB

System Properties Comparison Amazon Neptune vs. Google BigQuery vs. NSDb vs. SurrealDB

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Editorial information provided by DB-Engines

Name

Description

Fast, reliable graph database built for the cloud

Large scale data warehouse service with append-only tables

Scalable, High-performance Time Series DBMS designed for Real-time Analytics on top of Kubernetes

A fully ACID transactional, developer-friendly, multi-model DBMS

Primary database model

Graph DBMS
RDF store

Relational DBMS

Time Series DBMS

Document store
Graph 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	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.86
Rank	#203	Overall
	#34	Document stores
	#18	Graph DBMS

Website

aws.amazon.com/neptune

cloud.google.com/bigquery

nsdb.io

surrealdb.com

Technical documentation

aws.amazon.com/neptune/developer-resources

cloud.google.com/bigquery/docs

nsdb.io/Architecture

surrealdb.com/docs

Developer

Amazon

Google

SurrealDB Ltd

Initial release

2017

2010

2017

2022

Current release

v1.5.0, May 2024

License

Commercial or Open Source

commercial

Open Source

Apache Version 2.0

Open Source

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, Scala

Rust

Server operating systems

hosted

Linux
macOS

Linux
macOS
Windows

Data scheme

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language

APIs and other access methods

OpenCypher
RDF 1.1 / SPARQL 1.1
TinkerPop Gremlin

RESTful HTTP/JSON API

gRPC
HTTP REST
WebSocket

GraphQL
RESTful HTTP API
WebSocket

Supported programming languages

C#
Go
Java
JavaScript
PHP
Python
Ruby
Scala

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
Scala

Deno
Go
JavaScript (Node.js)
Rust

Server-side scripts

Stored procedures

user defined functions

in JavaScript

Triggers

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

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.

MapReduce

Offers an API for user-defined Map/Reduce methods

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual 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

yes

Using Apache Lucene

yes

In-memory capabilities

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

User concepts

Access control

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

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

Google Cloud Identity & Access Management (IAM)

yes, based on authentication and database rules

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