DBMS > Google BigQuery vs. Google Cloud Firestore vs. NCache vs. NSDb vs. SwayDB

System Properties Comparison Google BigQuery vs. Google Cloud Firestore vs. NCache vs. NSDb vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Cloud Firestore is an auto-scaling document database for storing, syncing, and querying data for mobile and web apps. It offers seamless integration with other Firebase and Google Cloud Platform products.

Open-Source and Enterprise in-memory Key-Value Store

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

An embeddable, non-blocking, type-safe key-value store for single or multiple disks and in-memory storage

Primary database model

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

Score	7.85
Rank	#51	Overall
	#8	Document stores

Score	0.94
Rank	#195	Overall
	#29	Key-value stores

Score	0.00
Rank	#383	Overall
	#41	Time Series DBMS

Score	0.00
Rank	#382	Overall
	#59	Key-value stores

Website

cloud.google.com/bigquery

firebase.google.com/products/firestore

www.alachisoft.com/ncache

nsdb.io

swaydb.simer.au

Technical documentation

cloud.google.com/bigquery/docs

firebase.google.com/docs/firestore

www.alachisoft.com/resources/docs

nsdb.io/Architecture

Developer

Google

Alachisoft

Simer Plaha

Initial release

2010

2017

2005

2017

2018

Current release

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Enterprise Edition available

Open Source

Apache Version 2.0

Open Source

GNU Affero GPL V3.0

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#, .NET, .NET Core, Java

Java, Scala

Scala

Server operating systems

hosted

Linux
Windows

Linux
macOS

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

Supported data types are Lists, Queues, Hashsets, Dictionary and Counter

yes: int, bigint, decimal, string

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

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query syntax and LINQ for searching the cache. Cache Synchronization with SQL Server using SQL dependency.

SQL-like query language

APIs and other access methods

RESTful HTTP/JSON API

Android
gRPC (using protocol buffers) API
iOS
JavaScript API
RESTful HTTP API

IDistributedCache
JCache
LINQ
Proprietary native API

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Go
Java
JavaScript
JavaScript (Node.js)
Objective-C
Python

.Net
.Net Core
C#
Java
JavaScript (Node.js)
Python
Scala

Java
Scala

Java
Kotlin
Scala

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes, Firebase Rules & Cloud Functions

support for stored procedures with SQL-Server CLR

Triggers

yes, with Cloud Functions

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Multi-source replication

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

Using Cloud Dataflow

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency
Immediate Consistency
Strong Eventual Consistency over WAN with Conflict Resolution using Bridge Topology

Eventual Consistency

Immediate Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

yes

optimistic locking and pessimistic locking

Atomic execution of operations

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

Using Apache Lucene

yes

In-memory capabilities

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

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

Access rights for users, groups and roles based on Google Cloud Identity and Access Management. Security Rules for 3rd party authentication using Firebase Auth.

Authentication to access the cache via Active Directory/LDAP (possible roles: user, administrator)

More information provided by the system vendor

Specific characteristics

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Competitive advantages

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Typical application scenarios

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Key customers

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
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Market metrics

Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

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