Year: 2010

Aster Data hires Quentin Gallivan as CEO

AsterData formally marked phase 2 of it’s rapid growth story by getting as new CEO Quentin Gallivan (of Postini before it was sold to Google and also Pivotlink).

Founders (and Stanfordians) Mayan Bawa stays as Chief Customer Officer and Tasso Argyros as CTO. It has a very deja vu feel -like Eric Schmidt coming in CEO of Google in the glory days past.  Indeed the investment team in Google and AsterData is quite similar and so are the backgrounds of the founders.

AsterData of course creates the leading MapReduce (also created by Google) solution for providing BI infrastructure for big data and has been rapidly been expanding into new frontiers for Big Data.

Aster Data Appoints New Chief Executive Officer

Quentin Gallivan Joins Aster Data as CEO to Lead Company to Next Level of Growth

San Carlos, CA – September 9, 2010– Aster Data, a proven leader dedicated to providing the best data management and data processing platform for big data management and analytics, today announced the appointment of Quentin Gallivan as President and CEO. Gallivan brings more than 20 years of senior executive experience to the leading analytics and database company. With Aster Data achieving tremendous growth in the past year, Gallivan will take Aster Data to the next level, further accelerating its market leadership, sales, channel partnerships and international expansion.  Founding CEO Mayank Bawa, who grew the company from its inception based on the founders’ research at Stanford University, and whose passion for helping customers uniquely unlock the value of their data, will take on the role of Chief Customer Officer.  Bawa, in his new role, will lead the Company’s organization devoted to ensuring the success, longevity and innovation of its fast-growing customer base. Together, Gallivan and Bawa, along with co-founder and Chief Technology Officer, Tasso Argyros, will deliver on the the Company’s mission to help customers discover more value from their data, achieve deep insights through rich analytics and do more with their massive data volumes than has ever been possible.

Gallivan joins Aster Data with over 20 years of leadership experience in the high-tech industry and has held a variety of CEO and senior executive positions with leading technology companies. Before joining Aster Data, Gallivan served as CEO at PivotLink, the leading provider of business intelligence (BI) solutions delivered via Software as a Service (SaaS), where he rapidly grew the company to over 15,000 business users, from mid-sized companies to Fortune 1000 companies, across key industries including financial services, retail, CPG manufacturing and high technology. Prior to Pivotlink, Gallivan served as CEO of Postini where he scaled the company to 35,000 customers and over 10 million users until its eventual acquisition by Google in 2007.  Gallivan also served as executive vice president of worldwide sales and services at VeriSign where he was instrumental in growing the business from $20 million to $1.2 billion and was responsible for the design and execution of the global distribution strategy for the company’s security and services business. Gallivan also held a number of key executive and leadership positions at Netscape Communications and GE Information Services.

“We are delighted to have someone of Quentin’s caliber, who is a veteran of both emerging and established technology companies, lead Aster Data through our next stage of growth,” said Mayank Bawa, Chief Customer Officer and co-founder, Aster Data. “His significant experience around growing organizations and driving operational excellence will be invaluable as he takes Aster Data forward. I’m excited to shift my focus to customers and their success; to bring our innovations to our customers worldwide to help them unlock deep value from their growing data volumes.”

“I am very excited to be joining Aster Data and taking on the challenge of augmenting its already impressive level of growth and success.  Aster Data is very well respected and established in the marketplace, has an enviable solution for big data management that uniquely addresses both big data storage and data processing, an impressive client list and a very talented team,” said Quentin Gallivan, President and CEO, Aster Data. “My task will be to leverage these assets, help shape a new market and provide operational guidance and strategic direction to drive even greater value for shareholders, customers and employees alike.”

Amazon announces Micro Instances for cloud computing

From Amazon http://aws.amazon.com/ec2

Micro instances provide 613 MB of memory and support 32-bit and 64-bit platforms on both Linux and Windows. Micro instance pricing for On-Demand instances starts at $0.02 per hour for Linux and $0.03 per hour for Windows.

Customers have asked us for a lower priced instance type that could satisfy the needs of their less demanding applications. Micro instances are optimized for applications that require lower throughput, but which still may consume significant compute cycles periodically. Micro instances provide a small amount of consistent CPU resources, and also allow you to burst CPU capacity when additional cycles are available.

Micro instances are available immediately in all regions, and we invite you to go and try one out for yourself today! Learn more about Amazon EC2’s new Micro instances ataws.amazon.com/ec2.

Micro Instances

Instances of this family provide a small amount of consistent CPU resources and allow you to burst CPU capacity when additional cycles are available. They are well suited for lower throughput applications and web sites that consume significant compute cycles periodically.

  • Micro Instance 613 MB of memory, up to 2 ECUs (for short periodic bursts), EBS storage only, 32-bit or 64-bit platform

So dont buy that new CPU yet- use existing hardware in tandem with these micro instances (and internet) to compute- (but  only if your corporate IP administrator wasn’t trained in Windows only certifications 😉

Event: Predictive analytics with R, PMML and ADAPA

From http://www.meetup.com/R-Users/calendar/14405407/

The September meeting is at the Oracle campus. (This is next door to the Oracle towers, so there is plenty of free parking.) The featured talk is from Alex Guazzelli (Vice President – Analytics, Zementis Inc.) who will talk about “Predictive analytics with R, PMML and ADAPA”.

Agenda:
* 6:15 – 7:00 Networking and Pizza (with thanks to Revolution Analytics)
* 7:00 – 8:00 Talk: Predictive analytics with R, PMML and ADAPA
* 8:00 – 8:30 General discussion

Talk overview:

The rule in the past was that whenever a model was built in a particular development environment, it remained in that environment forever, unless it was manually recoded to work somewhere else. This rule has been shattered with the advent of PMML (Predictive Modeling Markup Language). By providing a uniform standard to represent predictive models, PMML allows for the exchange of predictive solutions between different applications and various vendors.

Once exported as PMML files, models are readily available for deployment into an execution engine for scoring or classification. ADAPA is one example of such an engine. It takes in models expressed in PMML and transforms them into web-services. Models can be executed either remotely by using web-services calls, or via a web console. Users can also use an Excel add-in to score data from inside Excel using models built in R.

R models have been exported into PMML and uploaded in ADAPA for many different purposes. Use cases where clients have used the flexibility of R to develop and the PMML standard combined with ADAPA to deploy range from financial applications (e.g., risk, compliance, fraud) to energy applications for the smart grid. The ability to easily transition solutions developed in R to the operational IT production environment helps eliminate the traditional limitations of R, e.g. performance for high volume or real-time transactional systems and memory constraints associated with large data sets.

Speaker Bio:

Dr. Alex Guazzelli has co-authored the first book on PMML, the Predictive Model Markup Language which is the de facto standard used to represent predictive models. The book, entitled PMML in Action: Unleashing the Power of Open Standards for Data Mining and Predictive Analytics, is available on Amazon.com. As the Vice President of Analytics at Zementis, Inc., Dr. Guazzelli is responsible for developing core technology and analytical solutions under ADAPA, a PMML-based predictive decisioning platform that combines predictive analytics and business rules. ADAPA is the first system of its kind to be offered as a service on the cloud.
Prior to joining Zementis, Dr. Guazzelli was involved in not only building but also deploying predictive solutions for large financial and telecommunication institutions around the globe. In academia, Dr. Guazzelli worked with data mining, neural networks, expert systems and brain theory. His work in brain theory and computational neuroscience has appeared in many peer reviewed publications. At Zementis, Dr. Guazzelli and his team have been involved in a myriad of modeling projects for financial, health-care, gaming, chemical, and manufacturing industries.

Dr. Guazzelli holds a Ph.D. in Computer Science from the University of Southern California and a M.S and B.S. in Computer Science from the Federal University of Rio Grande do Sul, Brazil.

Movie Review: Lafangey Parinday (Rouge Birds)

 

Unlike earlier movies/reviews- this one is an out and out Bollywood masala movie- include suspense- drama-action-romance-songs. Neil Nitin Mukesh gives up his clean choco boy look to play a street boxer, who accidentally knocks down his neighbour Pinky Palakar (played by Deepika Padukone)- a feisty Mumbai gal who dreams of leaving her shanty by skate dancing into India Got Talent.- a TV show (where you vote by sms kinds). Girl goes blind- so boxer hero turns partner dancer to help her.  Rest is all song and dance.

Very forgettable time-pass movie- but overall feel good. Not all movies can be cerebal, na? Hero always wins 😉

Rapid Miner- R Extension

Here is a new video which shows exactly how you can use Rapid Miner and R together. Advantages of using both together is using Rapid Miner’s GUI (including the flowchart style for data mning) and adding R statistical functionality to it.

From http://rapid-i.com/content/view/219/1/

The web site features a video showing how easy R models and scripts can be integrated into the RapidMiner analysis processes. RapidMiner offers a new R perspective consisting of the known R console together with the great plotting facilities of R. All variables as well as R scripts can be stored in the RapidMiner Repository and used from there which helps to organize the usually large number of scripts. Furthermore, widely used modeling methods are directly integrated as RapidMiner operators as usual.

“This is a huge step for open source data analysis. RapidMiner offers a great user interface, a clear process structure and lots of ETL and analysis capabilities necessary for real-world problems. R adds a lot of flexibility and many analysis and data manipulation methods. The result is the by far most powerful data transformation and analysis solution worldwide. And this analysis power is now combined with the ease-of-use already known from RapidMiner.” states Dr. Ingo Mierswa, CEO of Rapid-I.

Visit the RCOMM 2010 and learn more about how to integrate analysis and preprocessing methods offered by R as well as how to use the new R perspective offering a full R console and access to all R plotters.

Thus Rapid Miner is one more mainstream software (after SPSS, SAS etc) to add R functionality to it.

KDNuggets Poll on SAS: Churn in Analytics Users

Here are the some surprising results from the Bible of all Data Miners , KDNuggets.com with some interesting comments about SAS being the Microsoft of analytics.

I believe technically advanced users will probably want to try out R before going in for a commercial license from Revolution Analytics as it is free to try out. Also WPS offers a one month free preview for its software- the latest release of it competes with SAS/Stat and SAS/Access, SAS/Graph and Base SAS- so anyone having these installations on a server would be interested to atleast test it for free. Also WPS would be interested in increasing engines (like they have for Oracle and Teradata).

One very crucial difference for SAS is it’s ability to pull in data from almost all data formats- so if you are using SAS/Connect to remote submit code- then you may not be able to switch soon.

Also the more license heavy customers are not the kind of cutomers who have lots of data in their local desktops but is usually pulled and then crunched before analysed. R has recently made some strides with the RevoScaler package from Revolution Analytics but it’s effectiveness would be tested and tried in the coming months- it seems like a great step in the right direction.

For SAS, the feedback should be a call to improve their product bundling – some of which can feel like over selling at times- but they have been fighting off challenges since past 4 decades and have the pockets and intention to sustain market share battles including discounts ( for repeat customers SAS can be much cheaper than say a first time user of WPS or R)

http://teamwpc.co.uk/home

This really should come as a surprise to some people. You can see the comments on WPS and R at the site itself. Interesting stufff and we can see after say 1 year to see how many actually DID switch.

http://www.kdnuggets.com/polls/2010/switching-from-sas-to-wps.html

Interview Stephanie McReynolds Director Product Marketing, AsterData

Here is an interview with Stephanie McReynolds who works as as Director of Product Marketing with AsterData. I asked her a couple of questions about the new product releases from AsterData in analytics and MapReduce.

Ajay – How does the new Eclipse Plugin help people who are already working with huge datasets but are new to AsterData’s platform?

Stephanie- Aster Data Developer Express, our new SQL-MapReduce development plug-in for Eclipse, makes MapReduce applications easy to develop. With Aster Data Developer Express, developers can develop, test and deploy a complete SQL-MapReduce application in under an hour. This is a significant increase in productivity over the traditional analytic application development process for Big Data applications, which requires significant time coding applications in low-level code and testing applications on sample data.

Ajay – What are the various analytical functions that are introduced by you recently- list say the top 10.

Stephanie- At Aster Data, we have an intense focus on making the development process easier for SQL-MapReduce applications. Aster Developer Express is a part of this initiative, as is the release of pre-defined analytic functions. We recently launched both a suite of analytic modules and a partnership program dedicated to delivering pre-defined analytic functions for the Aster Data nCluster platform. Pre-defined analytic functions delivered by Aster Data’s engineering team are delivered as modules within the Aster Data Analytic Foundation offering and include analytics in the areas of pattern matching, clustering, statistics, and text analysis– just to name a few areas. Partners like Fuzzy Logix and Cobi Systems are extending this library by delivering industry-focused analytics like Monte Carlo Simulations for Financial Services and geospatial analytics for Public Sector– to give you a few examples.

Ajay – So okay I want to do a K Means Cluster on say a million rows (and say 200 columns) using the Aster method. How do I go about it using the new plug-in as well as your product.

Stephanie- The power of the Aster Data environment for analytic application development is in SQL-MapReduce. SQL is a powerful analytic query standard because it is a declarative language. MapReduce is a powerful programming framework because it can support high performance parallel processing of Big Data and extreme expressiveness, by supporting a wide variety of programming languages, including Java, C/C#/C++, .Net, Python, etc. Aster Data has taken the performance and expressiveness of MapReduce and combined it with the familiar declarativeness of SQL. This unique combination ensures that anyone who knows standard SQL can access advanced analytic functions programmed for Big Data analysis using MapReduce techniques.

kMeans is a good example of an analytic function that we pre-package for developers as part of the Aster Data Analytic Foundation. What does that mean? It means that the MapReduce portion of the development cycle has been completed for you. Each pre-packaged Aster Data function can be called using standard SQL, and executes the defined analytic in a fully parallelized manner in the Aster Data database using MapReduce techniques. The result? High performance analytics with the expressiveness of low-level languages accessed through declarative SQL.

Ajay – I see an an increasing focus on Analytics. Is this part of your product strategy and how do you see yourself competing with pure analytics vendors.

Stephanie – Aster Data is an infrastructure provider. Our core product is a massively parallel processing database called nCluster that performs at or beyond the capabilities of any other analytic database in the market today. We developed our analytics strategy as a response to demand from our customers who were looking beyond the price/performance wars being fought today and wanted support for richer analytics from their database provider. Aster Data analytics are delivered in nCluster to enable analytic applications that are not possible in more traditional database architectures.

Ajay – Name some recent case studies in Analytics of implementation of MR-SQL with Analytical functions

Stephanie – There are three new classes of applications that Aster Data Express and Aster Analytic Foundation support: iterative analytics, prediction and optimization, and ad hoc analysis.

Aster Data customers are uncovering critical business patterns in Big Data by performing hypothesis-driven, iterative analytics. They are exploring interactively massive volumes of data—terabytes to petabytes—in a top-down deductive manner. ComScore, an Aster Data customer that performs website experience analysis is a good example of an Aster Data customer performing this type of analysis.

Other Aster Data customers are building applications for prediction and optimization that discover trends, patterns, and outliers in data sets. Examples of these types of applications are propensity to churn in telecommunications, proactive product and service recommendations in retail, and pricing and retention strategies in financial services. Full Tilt Poker, who is using Aster Data for fraud prevention is a good example of a customer in this space.

The final class of application that I would like to highlight is ad hoc analysis. Examples of ad hoc analysis that can be performed includes social network analysis, advanced click stream analysis, graph analysis, cluster analysis and a wide variety of mathematical, trigonometry, and statistical functions. LinkedIn, whose analysts and data scientists have access to all of their customer data in Aster Data are a good example of a customer using the system in this manner.

While Aster Data customers are using nCluster in a number of other ways, these three new classes of applications are areas in which we are seeing particularly innovative application development.

Biography-

Stephanie McReynolds is Director of Product Marketing at Aster Data, where she is an evangelist for Aster Data’s massively parallel data-analytics server product. Stephanie has over a decade of experience in product management and marketing for business intelligence, data warehouse, and complex event processing products at companies such as Oracle, Peoplesoft, and Business Objects. She holds both a master’s and undergraduate degree from Stanford University.