DBMS > Google BigQuery vs. KairosDB vs. Microsoft Access vs. NCache

System Properties Comparison Google BigQuery vs. KairosDB vs. Microsoft Access vs. NCache

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Editorial information provided by DB-Engines

Name

Description

Large scale data warehouse service with append-only tables

Distributed Time Series DBMS based on Cassandra or H2

Microsoft Access combines a backend RDBMS (JET / ACE Engine) with a GUI frontend for data manipulation and queries.

The Access frontend is often used for accessing other datasources (DBMS, Excel, etc.)

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

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	58.10
Rank	#19	Overall
	#13	Relational DBMS

Score	0.67
Rank	#233	Overall
	#20	Time Series DBMS

Score	101.16
Rank	#11	Overall
	#8	Relational DBMS

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

Website

cloud.google.com/bigquery

github.com/kairosdb/kairosdb

www.microsoft.com/en-us/microsoft-365/access

www.alachisoft.com/ncache

Technical documentation

cloud.google.com/bigquery/docs

kairosdb.github.io

developer.microsoft.com/en-us/access

www.alachisoft.com/resources/docs

Developer

Google

Microsoft

Alachisoft

Initial release

2010

2013

1992

2005

Current release

1.2.2, November 2018

1902 (16.0.11328.20222), March 2019

5.3.3, April 2024

License

Commercial or Open Source

commercial

Open Source

Apache 2.0

commercial

Bundled with Microsoft Office

Open Source

Enterprise Edition available

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++

C#, .NET, .NET Core, Java

Server operating systems

hosted

Linux
OS X
Windows

Windows

Not a real database server, but making use of DLLs

Linux
Windows

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

SQL

Support of SQL

yes

but not compliant to any SQL standard

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

APIs and other access methods

RESTful HTTP/JSON API

Graphite protocol
HTTP REST
Telnet API

ADO.NET
DAO
ODBC
OLE DB

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

Java
JavaScript

Node.js
PHP
Python

C
C#
C++
Delphi
Java (JDBC-ODBC)
VBA
Visual Basic.NET

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

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes

since Access 2010 using the ACE-engine

support for stored procedures with SQL-Server CLR

Triggers

yes

since Access 2010 using the ACE-engine

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

based on Cassandra

none

yes

Replication methods

Methods for redundantly storing data on multiple nodes

selectable replication factor

based on Cassandra

none

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

based on Cassandra
Immediate Consistency

based on Cassandra

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

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

but no files for transaction logging

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

but no files for transaction logging

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)

simple password-based access control

a simple user-level security was built in till version Access 2003

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

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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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