DBMS > Apache Ignite vs. Bangdb vs. Greenplum

System Properties Comparison Apache Ignite vs. Bangdb vs. Greenplum

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Editorial information provided by DB-Engines

Name

Description

Apache Ignite is a memory-centric distributed database, caching, and processing platform for transactional, analytical, and streaming workloads, delivering in-memory speeds at petabyte scale.

Converged and high performance database for device data, events, time series, document and graph

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.

Primary database model

Key-value store
Relational DBMS

Document store
Graph DBMS
Time Series DBMS

Relational DBMS

Secondary database models

Spatial DBMS

Document store
Spatial DBMS

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

Score	2.94
Rank	#92	Overall
	#14	Key-value stores
	#48	Relational DBMS

Score	0.06
Rank	#353	Overall
	#48	Document stores
	#36	Graph DBMS
	#35	Time Series DBMS

Score	7.39
Rank	#47	Overall
	#29	Relational DBMS

Website

ignite.apache.org

bangdb.com

greenplum.org

Technical documentation

apacheignite.readme.io/docs

docs.bangdb.com

docs.greenplum.org

Developer

Apache Software Foundation

Sachin Sinha, BangDB

Pivotal Software Inc.

Initial release

2015

2012

2005

Current release

2.16.0, December 2023

BangDB 2.0, October 2021

7.0.0, September 2023

License

Commercial or Open Source

Open Source

Apache 2.0

Open Source

BSD 3

Open Source

Apache 2.0

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

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Implementation language

C++, Java, .Net

C, C++

Server operating systems

Linux
OS X
Solaris
Windows

Linux

Data scheme

yes

schema-free

yes

Typing

predefined data types such as float or date

yes

yes: string, long, double, int, geospatial, stream, events

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.

yes

since Version 4.2

Secondary indexes

yes

secondary, composite, nested, reverse, geospatial

yes

SQL

Support of SQL

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

SQL like support with command line tool

yes

APIs and other access methods

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

Proprietary protocol
RESTful HTTP API

JDBC
ODBC

Supported programming languages

C#
C++
Java
PHP
Python
Ruby
Scala

C
C#
C++
Java
Python

C
Java
Perl
Python
R

Server-side scripts

Stored procedures

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

yes

Triggers

yes (cache interceptors and events)

yes, Notifications (with Streaming only)

yes

Partitioning methods

Methods for storing different data on different nodes

Sharding

Sharding (enterprise version only). P2P based virtual network overlay with consistent hashing and chord algorithm

Sharding

Replication methods

Methods for redundantly storing data on multiple nodes

yes (replicated cache)

selectable replication factor, Knob for CAP (enterprise version only)

Source-replica replication

MapReduce

Offers an API for user-defined Map/Reduce methods

yes (compute grid and hadoop accelerator)

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Tunable consistency, set CAP knob accordingly

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

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

ACID

Concurrency

Support for concurrent manipulation of data

yes

yes, optimistic concurrency control

yes

Durability

Support for making data persistent

yes

yes, implements WAL (Write ahead log) as well

yes

In-memory capabilities

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

yes

yes, run db with in-memory only mode

User concepts

Access control

Security Hooks for custom implementations

yes (enterprise version only)

fine grained access rights according to SQL-standard

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More resources

Recent citations in the news

What is Apache Ignite? How is Apache Ignite Used?
18 July 2022, The Stack

Real-time in-memory OLTP and Analytics with Apache Ignite on AWS | Amazon Web Services
14 May 2016, AWS Blog

GridGain In-Memory Data Fabric Becomes Apache Ignite
9 April 2015, Linux.com

Fire up big data processing with Apache Ignite
27 October 2016, InfoWorld

GridGain Releases Conference Schedule for Virtual Apache Ignite Summit 2023
1 June 2023, Datanami

provided by Google News

Chapter 1. Introducing the Greenplum Database
6 December 2018, O'Reilly Media

VMware Greenplum on AWS: Parallel Postgres for Enterprise Analytics at Scale
9 September 2019, AWS Blog

Greenplum 6 ventures outside the analytic box
19 March 2019, ZDNet

EMC and Greenplum Dress Elephant for IT Parade
8 December 2011, WIRED

EMC Introduces Breakthrough 'Big Data' Computing System
13 October 2010, PR Newswire

provided by Google News

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