AsterData still alive;/launches SQL-MapReduce Developer Portal

so apparantly ole client AsterData continues to thrive under gentle touch of Terrific Data

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Aster Data today launched the SQL-MapReduce Developer Portal, a new online community for data scientists and analytic developers. For your convenience, I copied the release below and it can also be found here. Please let me know if you have any questions or if there is anything else I can help you with.

Sara Korolevich

Point Communications Group for Aster Data

sarak@pointcgroup.com

Office: 602.279.1137

Mobile: 623.326.0881

Teradata Accelerates Big Data Analytics with First Collaborative Community for SQL-MapReduce®

New online community for data scientists and analytic developers enables development and sharing of powerful MapReduce analytics

San Carlos, California – Teradata Corporation (NYSE:TDC) today announced the launch of the Aster Data SQL-MapReduce® Developer Portal. This portal is the first collaborative online developer community for SQL-MapReduce analytics, an emerging framework for processing non-relational data and ultra-fast analytics.

“Aster Data continues to deliver on its unique vision for powerful analytics with a rich set of tools to make development of those analytics quick and easy,” said Tasso Argyros, vice president of Aster Data Marketing and Product Management, Teradata Corporation. “This new developer portal builds on Aster Data’s continuing SQL-MapReduce innovation, leveraging the flexibility and power of SQL-MapReduce for analytics that were previously impossible or impractical.”

The developer portal showcases the power and flexibility of Aster Data’s SQL-MapReduce – which uniquely combines standard SQL with the popular MapReduce distributed computing technology for processing big data – by providing a collaborative community for sharing SQL-MapReduce expert insights in addition to sharing SQL-MapReduce analytic functions and sample code. Data scientists, quantitative analysts, and developers can now leverage the experience, knowledge, and best practices of a community of experts to easily harness the power of SQL-MapReduce for big data analytics.

A recent report from IDC Research, “Taking Care of Your Quants: Focusing Data Warehousing Resources on Quantitative Analysts Matters,” has shown that by enabling data scientists with the tools to harness emerging types and sources of data, companies create significant competitive advantage and become leaders in their respective industry.

“The biggest positive differences among leaders and the rest come from the introduction of new types of data,” says Dan Vesset, program vice president, Business Analytics Solutions, IDC Research. “This may include either new transactional data sources or new external data feeds of transactional or multi-structured interactional data — the latter may include click stream or other data that is a by-product of social networking.”

Vesset goes on to say, “Aster Data provides a comprehensive platform for analytics and their SQL-MapReduce Developer Portal provides a community for sharing best practices and functions which can have an even greater impact to an organization’s business.”

With this announcement Aster Data extends its industry leadership in delivering the most comprehensive analytic platform for big data analytics — not only capable of processing massive volumes of multi-structured data, but also providing an extensive set of tools and capabilities that make it simple to leverage the power of MapReduce analytics. The Aster Data

SQL-MapReduce Developer Portal brings the power of SQL-MapReduce accessible to data scientists, quantitative analysis, and analytic developers by making it easy to share and collaborate with experts in developing SQL-MapReduce analytics. This portal builds on Aster Data’s history of SQL-MapReduce innovations, including:

The first deep integration of SQL with MapReduce
The first MapReduce support for .NET
The first integrated development environment, Aster Data
Developer Express
A comprehensive suite of analytic functions, Aster Data
Analytic Foundation

Aster Data’s patent-pending SQL-MapReduce enables analytic applications and functions that can deliver faster, deeper insights on terabytes to petabytes of data. These applications are implemented using MapReduce but delivered through standard SQL and business intelligence (BI) tools.

SQL-MapReduce makes it possible for data scientists and developers to empower business analysts with the ability to make informed decisions, incorporating vast amounts of data, regardless of query complexity or data type. Aster Data customers are using SQL-MapReduce for rich analytics including analytic applications for social network analysis, digital marketing optimization, and on-the-fly fraud detection and prevention.

“Collaboration is at the core of our success as one of the leading providers, and pioneers of social software,” said Navdeep Alam, director of Data Architecture at Mzinga. “We are pleased to be one of the early members of The Aster Data SQL-MapReduce Developer Portal, which will allow us the ability to share and leverage insights with others in using big data analytics to attain a deeper understanding of customers’ behavior and create competitive advantage for our business.”

SQL-MapReduce is one of the core capabilities within Aster Data’s flagship product. Aster DatanCluster™ 4.6, the industry’s first massively parallel processing (MPP) analytic platform has an integrated analytics engine that stores and processes both relational and non-relational data at scale. With Aster Data’s unique analytics framework that supports both SQL and
SQL-MapReduce™, customers benefit from rich, new analytics on large data volumes with complex data types. Aster Data analytic functions are embedded within the analytic platform and processed locally with data, which allows for faster data exploration. The SQL-MapReduce framework provides scalable fault-tolerance for new analytics, providing users with superior reliability, regardless of number of users, query size, or data types.

About Aster Data
Aster Data is a market leader in big data analytics, enabling the powerful combination of cost-effective storage and ultra-fast analysis of new sources and types of data. The Aster Data nCluster analytic platform is a massively parallel software solution that embeds MapReduce analytic processing with data stores for deeper insights on new data sources and types to deliver new analytic capabilities with breakthrough performance and scalability. Aster Data’s solution utilizes Aster Data’s patent-pending SQL-MapReduce to parallelize processing of data and applications and deliver rich analytic insights at scale. Companies including Barnes & Noble, Intuit, LinkedIn, Akamai, and MySpace use Aster Data to deliver applications such as digital marketing optimization, social network and relationship analysis, and fraud detection and prevention.

About Teradata
Teradata is the world’s leader in data warehousing and integrated marketing management through itsdatabase software, data warehouse appliances, and enterprise analytics. For more information, visitteradata.com.

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Teradata is a trademark or registered trademark of Teradata Corporation in the United States and other countries.

Protected: Happy Labour Day to American Stats-ical Association

Mash Hash Smash

Mash Hash Smash
To all the Larries of the world
the Joes, the Jims, the Steves
A shout out, much respect, you have unfurled.

Mash
First create a mash, an aggregated stash
Cross Domain platforms , the blurring of silo edges,
Intra department rivalries have no place in Klaatus world
Create a mash-up , shut up and draw
Do not swallow your own FUD in AAker’s brand indentity trap
Cognitive Bias leads to agency conflict
Ignore the little guys who bond in dorms at your peril,
Statistically the young still have more time than the boomers
to adapt , to change, to nurture the trees that stood for 300 million years

Hash
Congratulations, you got some money
Enjoy the lunch, the San Fransisco strip pubs, attention
Working furiously as the launch date approaches
Dont leave your prototype when you schmooze
remember your hommies and bro
Nice people last nice products have faltered
Everyone hates pompous software marketing
Create a hash tag in your blog
make title tag both tweet optimized and web search crawl able
If you are good to the search engine
It being fair will reward you well
On the internet phonies don’t last except in bubbles

Smash
When the economy is down is a good time to hire top talent at negotiable rates
When the economy is up is a good time to move from naive sales forecast models
The ego of the CEO is less valuable than the death of an elephant
Elephants dont dance but peacocks do
Use Fear Doubt Uncertainty selectively
Karma revolves FUD PIE on your face
be generous to schools you didnt study in
The poor have a right to college in India and China
Your medicine costs 1/10 th in Asia
Outsource but dont betray your nation
Outsource everything except your pride
Enjoy the show, welcome to the ride

New book on BigData Analytics and Data mining using #Rstats with a GUI

I am hoping to put this on my pre-ordered or Amazon Wish list. The book the common people who wanted to do data mining with , but were unable to ask aloud they didnt know much. It is written by the seminal Australian authority on data mining Dr Graham Williams whom I interviewed here at https://decisionstats.com/2009/01/13/interview-dr-graham-williams/

Data Mining for the masses using an ergonomically designed Graphical User Interface.

Thank you Springer. Thank you Dr Graham Williams

http://www.springer.com/statistics/physical+%26+information+science/book/978-1-4419-9889-7

Data Mining with Rattle and R

The Art of Excavating Data for Knowledge Discovery

Series: Use R

Williams, Graham

1st Edition., 2011, XX, 409 p. 150 illus. in color.

Softcover, ISBN 978-1-4419-9889-7

Due: August 29, 2011

54,95 €

ABOUT THIS BOOK
AUTHORS & EDITORS

Encourages the concept of programming with data – more than just pushing data through tools, but learning to live and breathe the data
Accessible to many readers and not necessarily just those with strong backgrounds in computer science or statistics
Details some of the more popular algorithms for data mining, as well as covering model evaluation and model deployment

Data mining is the art and science of intelligent data analysis. By building knowledge from information, data mining adds considerable value to the ever increasing stores of electronic data that abound today. In performing data mining many decisions need to be made regarding the choice of methodology, the choice of data, the choice of tools, and the choice of algorithms.

Throughout this book the reader is introduced to the basic concepts and some of the more popular algorithms of data mining. With a focus on the hands-on end-to-end process for data mining, Williams guides the reader through various capabilities of the easy to use, free, and open source Rattle Data Mining Software built on the sophisticated R Statistical Software. The focus on doing data mining rather than just reading about data mining is refreshing.

The book covers data understanding, data preparation, data refinement, model building, model evaluation, and practical deployment. The reader will learn to rapidly deliver a data mining project using software easily installed for free from the Internet. Coupling Rattle with R delivers a very sophisticated data mining environment with all the power, and more, of the many commercial offerings.

Content Level » Research

Keywords » Data mining

Related subjects » Physical & Information Science

Jump to JMP- the best statistical GUI software as per Google Search

This book just won an international award

producing graphs alongside results. In most cases, each page or two-page spread completes a JMP task, which maximizes the book’s utility as a reference.

Continue reading “Jump to JMP- the best statistical GUI software as per Google Search”

Revolution releases R Windows for Academics for free

Image via Wikipedia

Based on the official email from them, God bless the merry coders at Revo-

Revolution Analytics has just released Revolution R Enterprise 4.3 for 32-bit and 64-bit Windows, a significant step forward in enterprise data analytics. It features an updated RevoScaleR package for scalable, fast (multicore), and extensible data analysis with R. Revolution R Enterprise 4.3 for Windows also provides R 2.12.2, and includes an enhanced R Productivity Environment (RPE), a full-featured integrated development environment with visual debugging capabilities. Also available is an updated Windows release of our deployment server solution, RevoDeployR 1.2, designed to help you deliver R analytics via the Web.

As a registered user of the Academic version of Revolution R Enterprise for Windows, you can take advantage of these improvements by downloading and installing Revolution R Enterprise 4.3 today. You can install Revolution R Enterprise 4.3 side-by-side with your existing Revolution R Enterprise installations; there is no need to uninstall previous versions.

Try JMP for free in steps 1-2-3

Test a 30 day free trial of JMP, the beautiful software with the ugliest website.

In case you have never used JMP, but know the difference between a mean and a mode- take a look.

Step 1 Fill long and badly designed outdated form (note the blue lightening graphics design and font)

Step 2 See uselessly long message, as the website does require registration but it has not done any oAuth/SM easy registration even though they help sell software in the same campus on social media

Step 3 Wait for 352 mb TO DOWNLOAD without a bit torrent or mirror servers, or even a link for scheduling Download Accelerator-

Note internet connections can be lousy (globally not just in India) to categorize 352 mb of downloads as painful.

And after all the violence and double talk
There’s just a song in all the trouble and the strife

JMP is still the best easiest to use powerful Big Data software with extensions into R and SAS.

Please share:

Please share:

Data Mining with Rattle and R

ABOUT THIS BOOK

Please share:

producing graphs alongside results. In most cases, each page or two-page spread completes a JMP task, which maximizes the book’s utility as a reference.

Please share:

Please share:

Please share: