DBMS > Google BigQuery vs. Heroic vs. Linter vs. NCache vs. SWC-DB

System Properties Comparison Google BigQuery vs. Heroic vs. Linter vs. NCache vs. SWC-DB

Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Time Series DBMS built at Spotify based on Cassandra or Google Cloud Bigtable, and ElasticSearch

RDBMS for high security requirements

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

A high performance, scalable Wide Column DBMS

Primary database model

Secondary database models

Spatial DBMS

Document store
Search engine

Using distributed Lucene

Time Series DBMS

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

Score	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.46
Rank	#265	Overall
	#22	Time Series DBMS

Score	0.12
Rank	#350	Overall
	#152	Relational DBMS

Score	0.96
Rank	#195	Overall
	#29	Key-value stores

Score	0.08
Rank	#364	Overall
	#13	Wide column stores

Website

cloud.google.com/bigquery

github.com/spotify/heroic

linter.ru

www.alachisoft.com/ncache

github.com/kashirin-alex/swc-db
www.swcdb.org

Technical documentation

cloud.google.com/bigquery/docs

spotify.github.io/heroic

www.alachisoft.com/resources/docs

Developer

Google

Spotify

relex.ru

Alachisoft

Alex Kashirin

Initial release

2010

2014

1990

2005

2020

Current release

5.3.3, April 2024

0.5, April 2021

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Open Source

Enterprise Edition available

Open Source

GPL V3

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

Java

C and C++

C#, .NET, .NET Core, Java

C++

Server operating systems

hosted

AIX
Android
BSD
HP Open VMS
iOS
Linux
OS X
VxWorks
Windows

Linux
Windows

Linux

Data scheme

yes

schema-free

yes

schema-free

Typing

predefined data types such as float or date

yes

partial

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

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

via Elasticsearch

yes

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

HQL (Heroic Query Language, a JSON-based language)
HTTP API

ADO.NET
JDBC
LINQ
ODBC
OLE DB
Oracle Call Interface (OCI)

IDistributedCache
JCache
LINQ
Proprietary native API

Proprietary protocol
Thrift

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
C#
C++
Java
Perl
PHP
Python
Qt
Ruby
Tcl

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

C++

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

proprietary syntax with the possibility to convert from PL/SQL

support for stored procedures with SQL-Server CLR

Triggers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

yes

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Source-replica replication

yes, with selectable consistency level

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

Immediate Consistency

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

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

ACID

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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)

fine grained access rights according to SQL-standard

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

More information provided by the system vendor

Specific characteristics

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

NCache is 100% .NET/ .NET Core based which fully supports ASP.NET Core Sessions ,...
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Typical application scenarios

NCache enables industries like retail, finance, banking IoT, travel, ecommerce, healthcare...
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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.
» more

Licensing and pricing models

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