DBMS > BigObject vs. Google BigQuery vs. NCache vs. Quasardb vs. TimesTen

System Properties Comparison BigObject vs. Google BigQuery vs. NCache vs. Quasardb vs. TimesTen

Editorial information provided by DB-Engines

Name

Description

Analytic DBMS for real-time computations and queries

Large scale data warehouse service with append-only tables

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

Distributed, high-performance timeseries database

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Secondary database models

Document store
Search engine

Using distributed Lucene

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

Score	0.13
Rank	#333	Overall
	#147	Relational DBMS

Score	60.38
Rank	#19	Overall
	#13	Relational DBMS

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

Score	0.14
Rank	#332	Overall
	#29	Time Series DBMS

Score	1.31
Rank	#163	Overall
	#74	Relational DBMS

Website

bigobject.io

cloud.google.com/bigquery

www.alachisoft.com/ncache

quasar.ai

www.oracle.com/database/technologies/related/timesten.html

Technical documentation

docs.bigobject.io

cloud.google.com/bigquery/docs

www.alachisoft.com/resources/docs

doc.quasar.ai/master

docs.oracle.com/database/timesten-18.1

Developer

BigObject, Inc.

Google

Alachisoft

quasardb

Oracle, TimesTen Performance Software, HP

originally founded in HP Labs it was acquired by Oracle in 2005

Initial release

2015

2010

2005

2009

1998

Current release

5.3.3, April 2024

3.14.1, January 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Enterprise Edition available

commercial

Free community edition, Non-profit organizations and non-commercial usage are eligible for free licenses

commercial

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

C++

Server operating systems

Linux

distributed as a docker-image
OS X

distributed as a docker-image (boot2docker)
Windows

distributed as a docker-image (boot2docker)

hosted

Linux
Windows

BSD
Linux
OS X
Windows

AIX
HP-UX
Linux
OS X
Solaris SPARC/x86
Windows

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

partial

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

yes

integer and binary

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

yes

with tags

yes

SQL

Support of SQL

SQL-like DML and DDL statements

yes

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

SQL-like query language

yes

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

IDistributedCache
JCache
LINQ
Proprietary native API

HTTP API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

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

.Net
C
C#
C++
Go
Java
JavaScript (Node.js)
PHP
Python
R

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

support for stored procedures with SQL-Server CLR

PL/SQL

Triggers

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

yes

Sharding

consistent hashing

none

Replication methods

Methods for redundantly storing data on multiple nodes

none

yes, with selectable consistency level

Source-replica replication with selectable replication factor

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

with Hadoop integration

Consistency concepts

Methods to ensure consistency in a distributed system

none

Immediate Consistency

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

Immediate Consistency

Immediate Consistency or Eventual Consistency depending on configuration

Foreign keys

Referential integrity

yes

automatically between fact table and dimension tables

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

yes

Durability

Support for making data persistent

yes

by using LevelDB

yes

by means of logfiles and checkpoints

In-memory capabilities

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

yes

Transient mode

yes

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

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

Cryptographically strong user authentication and audit trail

fine grained access rights according to SQL-standard

More information provided by the system vendor

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