DBMS > Google BigQuery vs. Hyprcubd vs. Microsoft Azure Cosmos DB vs. NCache vs. TimesTen

System Properties Comparison Google BigQuery vs. Hyprcubd vs. Microsoft Azure Cosmos DB vs. NCache vs. TimesTen

Editorial information provided by DB-Engines

Name

Hyprcubd seems to be discontinued. Therefore it is excluded from the DB-Engines ranking.

Description

Large scale data warehouse service with append-only tables

Serverless Time Series DBMS

Globally distributed, horizontally scalable, multi-model database service

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

In-Memory RDBMS compatible to Oracle

Primary database model

Relational DBMS

Time Series DBMS

Document store
Graph DBMS
Key-value store
Wide column store

Key-value store

Relational DBMS

Secondary database models

Spatial DBMS

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	27.71
Rank	#27	Overall
	#4	Document stores
	#2	Graph DBMS
	#3	Key-value stores
	#3	Wide column stores

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

Score	1.36
Rank	#161	Overall
	#75	Relational DBMS

Website

cloud.google.com/bigquery

hyprcubd.com (offline)

azure.microsoft.com/services/cosmos-db

www.alachisoft.com/ncache

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

Technical documentation

cloud.google.com/bigquery/docs

learn.microsoft.com/azure/cosmos-db

www.alachisoft.com/resources/docs

docs.oracle.com/database/timesten-18.1

Developer

Google

Hyprcubd, Inc.

Microsoft

Alachisoft

Oracle, TimesTen Performance Software, HP

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

Initial release

2010

2014

2005

1998

Current release

5.3.3, April 2024

11 Release 2 (11.2.2.8.0)

License

Commercial or Open Source

commercial

Open Source

Enterprise Edition available

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

Server operating systems

hosted

Linux
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

time, int, uint, float, string

yes

JSON types

partial

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

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

All properties auto-indexed by default

yes

SQL

Support of SQL

yes

SQL-like query language

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

yes

APIs and other access methods

RESTful HTTP/JSON API

gRPC (https)

DocumentDB API
Graph API (Gremlin)
MongoDB API
RESTful HTTP API
Table API

IDistributedCache
JCache
LINQ
Proprietary native API

JDBC
ODBC
ODP.NET
Oracle Call Interface (OCI)

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

.Net
C#
Java
JavaScript
JavaScript (Node.js)
MongoDB client drivers written for various programming languages
Python

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

C
C++
Java
PL/SQL

Server-side scripts

Stored procedures

user defined functions

in JavaScript

JavaScript

support for stored procedures with SQL-Server CLR

PL/SQL

Triggers

JavaScript

yes

Notifications

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

Implicit feature of the cloud service

yes

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes

Implicit feature of the cloud service

yes, with selectable consistency level

Multi-source replication
Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

with Hadoop integration

Integration with Hadoop/HDInsight on Azure*

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Bounded Staleness
Consistent Prefix
Eventual Consistency
Immediate Consistency

Consistency level configurable on request level
Session Consistency

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

Immediate Consistency or Eventual Consistency depending on configuration

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

Multi-item ACID transactions with snapshot isolation within a partition

optimistic locking and pessimistic locking

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

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

User concepts

Access control

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

Google Cloud Identity & Access Management (IAM)

token access

Access rights can be defined down to the item level

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

fine grained access rights according to SQL-standard

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

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Licensing and pricing models

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