Radoop 0.3 launched- Open Source Graphical Analytics meets Big Data

What is Radoop? Quite possibly an exciting mix of analytics and big data computing



What is Radoop?

Hadoop is an excellent tool for analyzing large data sets, but it lacks an easy-to-use graphical interface. RapidMiner is an excellent tool for data analytics, but its data size is limited by the memory available, and a single machine is often not enough to run the analyses on time. In this project, we combine the strengths of both projects and provide a RapidMiner extension for editing and running ETL, data analytics and machine learning processes over Hadoop.

We have closely integrated the highly optimized data analytics capabilities of Hive and Mahout, and the user-friendly interface of RapidMiner to form a powerful and easy-to-use data analytics solution for Hadoop.


and what’s new


Radoop 0.3 released – fully graphical big data analytics

Today, Radoop had a major step forward with its 0.3 release. The new version of the visual big data analytics package adds full support for all major Hadoop distributions used these days: Apache Hadoop 0.20.2, 0.20.203, 1.0 and Cloudera’s Distribution including Apache Hadoop 3 (CDH3). It also adds support for large clusters by allowing the namenode, the jobtracker and the Hive server to reside on different nodes.

As Radoop’s promise is to make big data analytics easier, the 0.3 release is also focused on improving the user interface. It has an enhanced breakpointing system which allows to investigate intermediate results, and it adds dozens of quick fixes, so common process design mistakes get much easier to solve.

There are many further improvements and fixes, so please consult the release notes for more details. Radoop is in private beta mode, but heading towards a public release in Q2 2012. If you would like to get early access, then please apply at the signup page or describe your use case in email (beta at radoop.eu).

Radoop 0.3 (15 February 2012)

  • Support for Apache Hadoop 0.20.2, 0.20.203, 1.0 and Cloudera’s Distribution Including Apache Hadoop 3 (CDH3) in a single release
  • Support for clusters with separate master nodes (namenode, jobtracker, Hive server)
  • Enhanced breakpointing to evaluate intermediate results
  • Dozens of quick fixes for the most common process design errors
  • Improved process design and error reporting
  • New welcome perspective to help in the first steps
  • Many bugfixes and performance improvements

Radoop 0.2.2 (6 December 2011)

  • More Aggregate functions and distinct option
  • Generate ID operator for convenience
  • Numerous bug fixes and improvements
  • Improved user interface

Radoop 0.2.1 (16 September 2011)

  • Set Role and Data Multiplier operators
  • Management panel for testing Hadoop connections
  • Stability improvements for Hive access
  • Further small bugfixes and improvements

Radoop 0.2 (26 July 2011)

  • Three new algoritms: Fuzzy K-Means, Canopy, and Dirichlet clustering
  • Three new data preprocessing operators: Normalize, Replace, and Replace Missing Values
  • Significant speed improvements in data transmission and interactive analytics
  • Increased stability and speedup for K-Means
  • More flexible settings for Join operations
  • More meaningful error messages
  • Other small bugfixes and improvements

Radoop 0.1 (14 June 2011)

Initial release with 26 operators for data transmission, data preprocessing, and one clustering algorithm.

Note that Rapid Miner also has a great R extension so you can use R, a graphical interface and big data analytics is now easier and more powerful than ever.

Introducing Radoop

Thats Right- This is Radoop and it is

Hadoop meats Rapid Miner=Radoop






Who searches for this Blog?

Statue of Michael Jackson in Eindhoven, the Ne...
Image via Wikipedia

Using WP- Stats I set about answering this question-

What search keywords lead here-

Clearly Michael Jackson is down this year

And R GUI, Data Mining is up.

How does that affect my writing- given I get almost 250 visitors by search engines alone daily- assume I write nothing on this blog from now on.

It doesnt- I still write what ever code or poem that comes to my mind. So it is hurtful people misunderstimate the effort in writing and jump to conclusions (esp if I write about a company- I am not on payroll of that company- just like if  I write about a poem- I am not a full time poet)

Over to xkcd

All Time (for Decisionstats.Wordpress.com)

Search Views
libre office 818
facebook analytics 806
michael jackson history 240
wps sas lawsuit 180
r gui 168
wps sas 154
wordle.net 118
sas wps 116
decision stats 110
sas wps lawsuit 100
google maps jet ski 94
data mining 88
doug savage 72
hive tutorial 63
spss certification 63
hadley wickham 63
google maps jetski 62
sas sues wps 60
decisionstats 58
donald farmer microsoft 45
libreoffice 44
wps statistics 44
best statistics software 42
r gui ubuntu 41
rstat 37
tamilnadu advanced technical training institute tatti 37


2009-11-24 to Today

Search Views
libre office 818
facebook analytics 781
wps sas lawsuit 170
r gui 164
wps sas 125
wordle.net 118
sas wps 101
sas wps lawsuit 95
google maps jet ski 94
data mining 86
decision stats 82
doug savage 63
hadley wickham 63
google maps jetski 62
hive tutorial 56
donald farmer microsoft 45

Hive Tutorial: Cloud Computing

Here is a nice video from Cloudera on a HIVE tutorial. I wonder what would happen if they put a real analytical system and not just basic analytics and reporting … like R or SPSS or JMP or SAS on big database system like Hadoop (including some text mined data from legacy company documents)

Unlike Oracle or other data base systems, Hadoop is free now and in reasonable future  (like MySQL used to be before acquired by big fish Sun acquired by bigger Oracle).



Hive is a data warehouse infrastructure built on top of Hadoop that provides tools to enable easy data summarization, adhoc querying and analysis of large datasets data stored in Hadoop files

Hive is based on Hadoop which is a batch processing system. Accordingly, this system does not and cannot promise low latencies on queries. The paradigm here is strictly of submitting jobs and being notified when the jobs are completed as opposed to real time queries. As a result it should not be compared with systems like Oracle where analysis is done on a significantly smaller amount of data but the analysis proceeds much more iteratively with the response times between iterations being less than a few minutes. For Hive queries response times for even the smallest jobs can be of the order of 5-10 minutes and for larger jobs this may even run into hours.

If your input data is small you can execute a query in a short time. For example, if a table has 100 rows you can ‘set mapred.reduce.tasks=1’ and ‘set mapred.map.tasks=1’ and the query time will be ~15 seconds.

Facebook Text Analytics

Here is a great presentation on Facebook Analytics using text mining.

Citation;Text Analytics Summit 2009 – Roddy Lindsay – “Social Media, Happiness, Petabytes and LOLs”
and here is a presentation on HIVE and HADOOP
HIVE: Data Warehousing & Analytics on Hadoop

Facebook sure looks a surprisingly nice analytics company to work for.!!! No wonder they have all but swamped the competition.