DBMS > Google BigQuery vs. GridGain vs. Hyprcubd vs. UniData,UniVerse

System Properties Comparison Google BigQuery vs. GridGain vs. Hyprcubd vs. UniData,UniVerse

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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

GridGain is an in-memory computing platform, built on Apache Ignite

Serverless Time Series DBMS

MultiValue database and application server with SQL mapping layer and meta database capabilities

Primary database model

Relational DBMS

Key-value store
Relational DBMS

Time Series DBMS

Multivalue DBMS

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

Score	61.90
Rank	#19	Overall
	#13	Relational DBMS

Score	1.53
Rank	#155	Overall
	#26	Key-value stores
	#73	Relational DBMS

Score	3.47
Rank	#93	Overall
	#2	Multivalue DBMS

Website

cloud.google.com/bigquery

www.gridgain.com

hyprcubd.com (offline)

www.rocketsoftware.com/products/rocket-multivalue-application-development-platform/rocket-unidata

Technical documentation

cloud.google.com/bigquery/docs

www.gridgain.com/docs/index.html

docs.rocketsoftware.com/bundle?cluster=true&labelkey=unidata&labelkey=prod_unidata

Developer

Google

GridGain Systems, Inc.

Hyprcubd, Inc.

Rocket Software

Initial release

2010

2007

1985

Current release

GridGain 8.5.1

License

Commercial or Open Source

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

Java, C++, .Net

Server operating systems

hosted

Linux
OS X
Solaris
Windows

hosted

AIX
HP-UX
Linux
Solaris
Windows

Data scheme

yes

schema-free

Typing

predefined data types such as float or date

yes

time, int, uint, float, string

optional

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

SQL

Support of SQL

yes

ANSI-99 for query and DML statements, subset of DDL

SQL-like query language

yes

APIs and other access methods

RESTful HTTP/JSON API

HDFS API
Hibernate
JCache
JDBC
ODBC
Proprietary protocol
RESTful HTTP API
Spring Data

gRPC (https)

Java API

JPA
JDBC
ODBC
OLE DB
Proprietary protocol
RESTful HTTP API
SOAP-based API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C#
C++
Java
PHP
Python
Ruby
Scala

.Net
Basic

U2 Basic
C
Java

Server-side scripts

Stored procedures

user defined functions

in JavaScript

yes (compute grid and cache interceptors can be used instead)

yes

Triggers

yes (cache interceptors and events)

yes

U2 Basic

Partitioning methods

Methods for storing different data on different nodes

none

Sharding

none

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Eventual Consistency

Foreign keys

Referential integrity

Transaction concepts

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

Since BigQuery is designed for querying data

ACID

configurable

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)

Security Hooks for custom implementations

token access

Access rights according to SQL-standard and operating system based

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