DBMS > Google BigQuery vs. Kdb vs. NCache vs. NSDb vs. SwayDB

System Properties Comparison Google BigQuery vs. Kdb vs. NCache vs. NSDb vs. SwayDB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

High performance Time Series DBMS

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

Relational DBMS

Time Series DBMS
Vector DBMS

Key-value store

Time Series DBMS

Key-value store

Secondary database models

Relational DBMS

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	7.55
Rank	#53	Overall
	#3	Time Series DBMS
	#1	Vector DBMS

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

kx.com

www.alachisoft.com/ncache

nsdb.io

swaydb.simer.au

Technical documentation

cloud.google.com/bigquery/docs

code.kx.com

www.alachisoft.com/resources/docs

nsdb.io/Architecture

Developer

Google

Kx Systems, a division of First Derivatives plc

Alachisoft

Simer Plaha

Initial release

2010

2000

kdb was released 2000, kdb+ in 2003

2005

2017

2018

Current release

3.6, May 2018

5.3.3, April 2024

License

Commercial or Open Source

commercial

free 32-bit version

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
OS X
Solaris
Windows

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.

yes

Secondary indexes

yes

table attribute 'grouped'

yes

all fields are automatically indexed

SQL

Support of SQL

yes

SQL-like query language (q)

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

HTTP API
JDBC
Jupyter
Kafka
ODBC
WebSocket

IDistributedCache
JCache
LINQ
Proprietary native API

gRPC
HTTP REST
WebSocket

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Go
J
Java
JavaScript
Lua
MatLab
Perl
PHP
Python
R
Scala

.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

user defined functions

support for stored procedures with SQL-Server CLR

Triggers

yes

with views

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

horizontal partitioning

yes

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

yes, with selectable consistency level

none

MapReduce

Offers an API for user-defined Map/Reduce methods

similar paradigm used for internal processing

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

yes

Transaction concepts

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

Since BigQuery is designed for querying data

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)

rights management via user accounts

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

More information provided by the system vendor

Specific characteristics

Integrated columnar database & programming system for streaming, real time and historical...
» more

NCache has been the market leader in .NET Distributed Caching since 2005 . NCache...
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Competitive advantages

provides seamless scalability; runs on industry standard server platforms; is top-ranked...
» more

NCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
» more

Typical application scenarios

tick database streaming sensor data massive intelligence applications oil and gas...
» more

NCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
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Key customers

Goldman Sachs Morgan Stanley Merrill Lynch J.P. Morgan Deutsche Bank IEX Securities...
» more

Bank of America, Citi, Natures Way, Charter Spectrum, Barclays, Henry Schein, GBM,...
» more

Market metrics

kdb+ performance and reliability proven by our customers in critical infrastructure...
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Market Leader in .NET Distributed Caching since 2005.
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Licensing and pricing models

upon request
» more

NCache Open Source is free on an as-is basis without any support. NCache Enterprise...
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