DBMS > BigObject vs. Google BigQuery vs. Greenplum vs. Infobright vs. NCache

System Properties Comparison BigObject vs. Google BigQuery vs. Greenplum vs. Infobright vs. NCache

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

Analytic Database platform built on PostgreSQL. Full name is Pivotal Greenplum Database

A logical database in Greenplum is an array of individual PostgreSQL databases working together to present a single database image.

High performant column-oriented DBMS for analytic workloads using MySQL or PostgreSQL as a frontend

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

Primary database model

Relational DBMS

a hierachical model (tree) can be imposed

Relational DBMS

Key-value store

Secondary database models

Document store
Spatial DBMS

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	8.37
Rank	#48	Overall
	#30	Relational DBMS

Score	0.96
Rank	#194	Overall
	#91	Relational DBMS

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

Website

bigobject.io

cloud.google.com/bigquery

greenplum.org

ignitetech.com/softwarelibrary/infobrightdb

www.alachisoft.com/ncache

Technical documentation

docs.bigobject.io

cloud.google.com/bigquery/docs

docs.greenplum.org

www.alachisoft.com/resources/docs

Developer

BigObject, Inc.

Google

Pivotal Software Inc.

Ignite Technologies Inc.; formerly InfoBright Inc.

Alachisoft

Initial release

2015

2010

2005

Current release

7.0.0, September 2023

5.3.3, April 2024

License

Commercial or Open Source

commercial

free community edition available

commercial

Open Source

Apache 2.0

commercial

The open source (GPLv2) version did not support inserts/updates/deletes and was discontinued with July 2016

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

C#, .NET, .NET Core, Java

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

Linux
Windows

Data scheme

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.

yes

since Version 4.2

Secondary indexes

yes

Knowledge Grid Technology used instead

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.

APIs and other access methods

fluentd
ODBC
RESTful HTTP API

RESTful HTTP/JSON API

JDBC
ODBC

ADO.NET
JDBC
ODBC

IDistributedCache
JCache
LINQ
Proprietary native API

Supported programming languages

.Net
Java
JavaScript
Objective-C
PHP
Python
Ruby

C
Java
Perl
Python
R

.Net
C
C#
C++
D
Eiffel
Erlang
Haskell
Java
Objective-C
OCaml
Perl
PHP
Python
Ruby
Scheme
Tcl

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

Server-side scripts

Stored procedures

Lua

user defined functions

in JavaScript

yes

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

Replication methods

Methods for redundantly storing data on multiple nodes

none

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

none

Immediate Consistency

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

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

ACID

optimistic locking and pessimistic locking

Concurrency

Support for concurrent manipulation of data

yes

Read/write lock on objects (tables, trees)

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

exploiting MySQL or PostgreSQL frontend capabilities

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

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Licensing and pricing models

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