Along with the release of Aster Data nCluster 4.5, we added a new Solution Partner level for systems integrators and developers.

Why is this relevant?

Recession or no-recession, IT executives are constantly challenged. They are asked to execute strategies based on better analytics and information to improve effectiveness of business processes (customer loyalty, inventory management, revenue optimization, ..), while staying on top of technology-based disruptions and managing (shrinking or flat) IT budgets.

IT organizations have taken on the challenge by building analytics-based offerings  leveraging existing data management skills and increasingly taking advantage of MapReduce, a disruptive technology introduced by Google and now being rapidly adopted by mainstream enterprise IT shops in Finance, Telco, LifeSciences, Govt. and other verticals.

As MapReduce and big data analytics goes mainstream, our customers and ecosystem partners have asked us to make it easier for their teams to leverage MapReduce across enterprise application lifecycles, while harvesting existing IT skills in SQL, Java and other programming languages.  The Aster development team that brought us the SQL/MapReduce innovation, has now delivered the market’s first integrated visual development environment for developing, deploying and managing MapReduce and SQL-based analytic applications.

Enterprise MapReduce developers and system integrators can now leverage the integrated Aster platform and deliver compelling business results in record time (read how ComScore delivers 360 degree view of digital world to enterprise customers, Full Tilt Poker gains the upper hand tackling online fraud using Aster).

We are also teaming up with leaders in our ecosystem like MicroStrategy to deliver an end-to-end analytics solution to our customers that includes SQL/MapReduce enabled reporting and rich visualization. Aster is proud to be driving innovation in the Analytics and BI market and was recently honored at  MicroStrategy’s annual customer conference.

I am delighted with the rapid adoption of Aster Data’s platform by our partners and the strong continued interest from enterprise developers and system integrators in building big data applications using Aster. New partners are endorsing our vision and technical innovation as the future of advanced analytics for large data volumes.

Sign up today to be an Aster solution partner and join the revolution to deliver compelling information and analytics-driven solutions.

1) Intuit (formerly mint.com) will be talking about how they anonymize consumer data and provide financial benchmarks and insights to individuals.  e.g., Do you spend more on dinner than the average Joe or Jane in California?

2)  Mobclix, a mobile advertising network, will discuss how they enable application developers for the iPhone, Android, BlackBerry, etc. to make more money by providing targeting advertising based on better analytics. (And how they’ve deployed it on Amazon Web Services to scale up & down on demand)

3) Merv Adrian from IT Market Strategy will keynote to talk about trends in data management and the questions you need to ask yourself as you consider ways to tackle big data problems. He’s also moderating a panel that I’m hearing will have some cameo appearances from other industry analysts with big brains.

4) Dell is our platinum sponsor and they’ll be talking about some new hardware they’re rolling out for big data computing and putting on display at the show.

5) Our CEO and co-founder will be talking about our approach to harnessing the power of big data and giving a glimpse of the future from Aster Data.

6) Other sponsors include Amazon Web Services, Informatica, and a new partner of ours, Impetus.

Hope to see you at the show.

Last week I attended Bank of America’s Technology Innovation Summit in Silicon Valley. In attendance were leading technology executives from Bank of America who outlined needs and challenges for the global banking giant. BofA’s annual IT spend is greater than $5 Billion, serving  almost 59 million, or one out of every two U.S. households and distribution strength of about six thousand branches, 18,000 ATMs and 24 million online banking customers, and more than 3,000 customer touches every second. Key themes discussed involved Cloud computing, Information Management, Security, Mobility and Green IT. And as I sat through the panel discussions and spoke to some of the IT leaders, it became evident that underpinning all the major business and IT initiatives for the global bank was a central theme – Lots of data, need for better and faster insights.

A senior BofA IT executive stated “Broad BI and data mining remain objectives, not realized goals”. There was a high level of interest in analytics and a big drive to be information-driven across business units.

Clearly, for a large bank like BofA, the business drivers exist. For example, the consumer channels executive was interested in understanding consumer behavior across different channels. In a saturated marketplace for retail customers and facing stiff competition from Chase (now owns WaMu), Wells Fargo (now owns Wachovia), BofA is keenly interested in strengthening its bond with its existing customer base.  With thousands of interactions per second, every interaction with the customer is an opportunity to learn more about customer behavior and customer preferences.

 In the credit card division, early detection of fraud patterns can translate into big savings for a market that is undergoing dramatic transformation due to reforms mandated by Congress. 

On the IT front, BofA has lots of existing investments in BI tools and data management software.

So where is the gap? Why are BI/data mining unrealized goals?

The answer lies in re-thinking and challenging the status quo in data management and analytic application development in today’s big data IT environments. Google, Amazon, and other innovators are leading this and it is only a matter of time before leaders in the financial services industry follow suit. A new mandate and architecture for big data applications  is emerging.

This new class of analytic applications will require a strategic investment in infrastructure that embraces assimilating advanced analytics processing right next to the terabytes to petabytes of enterprise data for key business initiatives including

• Customer service effectiveness to predict customer requirements as well as fully understand customer relationships across branch office, ATM, online, and mobile channels
• Ability to respond faster to regulators or to management and driving decisions based on insights driven from accurate, timely data

Broader, more pervasive BI and richer Analytics is on the threshold of becoming a reality!

At the same time, our CEO and co-founder Mayank will be sharing his thoughts on a separate blog called “Winning with Data” where he will talk about his perspectives of the market trends, customer use-cases, technology evolution and company growth. You can find all of his previous posts there, as well as fresh content starting with the announcement of Aster Data’s massively parallel data-application server.

- What is Aster Data’s SQL-MapReduce?
- Example industry applications of SQL-MapReduce
- Walking through the SQL-MapReduce syntax

Curt has also posted his slides on DBMS2 with a great overview on dispelling the myths around MapReduce, and how MapReduce and SQL play nicely with each other.

We had great turn-out and questions from the sessions.  If you have any questions after reviewing the material, please drop a comment.

In addition to the Big Data Summit we held in New York City (which we’ll detail in a separate post), Colin White presented on a Webcast with Aster on the various use-cases for Hadoop within data warehouse environments. Colin does a great job summarizing what Hadoop is, how it’s different from an RDBMS, the different types of users for each, and how they co-exist nicely in customer environments.

Below are the slides to view if you weren’t able to attend the event, which will also be available for on-demand viewing soon in our resource library.

To kick off the festivities, we’re holding a live webinar earlier in the day at 9 a.m. US Pacific time. Colin White and myself will be discussing Hadoop and data warehousing - how they’re similar, how they’re different, and what they can be used for (both separately and together). In fact, we’ll be making an important announcement of a new product offering that you won’t want to miss. If you haven’t already, I urge you to register by clicking here.

Mark it on your calendar - something “big” is coming on October 1st.

Aster is now in a new phase, when maximal focus on product delivery and responding to customers’ every need is taking center stage. We have a fantastic product that is growing quickly and is being deployed at many, many customer sites. The level of product focus in our team is astounding. Yes, my friends, the garage days are now officially over! … And I’m delighted we’ve come this far.

One should always invest their energy in those activities that most benefit from that energy. Aster is a grown-up now and is solidly set on a path with great momentum toward executing our “big data” vision. At the same time, I have a budding team of incredibly bright students back in my lab at EPFL, who are hungry for my help and scientific direction.  For me, it’s time for the next “garage project.”

As you might imagine, this is not an easy decision for a founder. Perhaps this is how parents feel when sending their kid to college away from home… but time does not stand still.

It has been a thrill to start Aster with Mayank and Tasso, see her emerge from 5 PCs cooled by a portable fan in my living room, and then working for her all these years. I’m proud of what we’ve achieved so far, both in terms of product and market impact, as well as our published research papers. At a future stage in Aster’s growth, it may make sense for me to resume my role in leading the company’s long-term research. Until then, I look forward to helping the company from behind the scenes and to watch Aster grow and develop the best data management platform in the market.

Carpe datum!

We also announced today that our first customer in Europe is the #1 online poker gaming site in the world, Full Tilt Poker. We have been working with Full Tilt Poker for 8 months now helping deploy Aster nCluster to power their fraud prevention systems and provide enhanced customer service to their players.

It is no surprise that data size growth is a world-wide phenomenon, and certainly occurs across “the pond” as well. We have noticed that European customers in numerous industries, such as financial services and insurance, online retailing, social networking, communications, and gaming are deploying new (and sometimes custom) applications to leverage big data.

Aster Data is certainly the most application friendly big-data infrastructure in the market, and we look forward to working with our European customers in the coming years!

This post was co-authored by John Cieslewicz, Eric Friedman, and Peter Pawlowski of Aster Data Systems

One year ago we introduced SQL/MapReduce for the Aster nCluster database, which integrates MapReduce and SQL to enable deep analytics within the database. Pushing computation inside the database and close to the data is increasingly important as data sizes grow exponentially. As SQL/MR turns one year old, we are happy to announce that we will be presenting our SQL/MR innovations next week at the 35th International Conference on Very Large Data Bases (VLDB), the premier international forum for database research.

The title of our conference paper is SQL/MapReduce: A Practical Approach to Self-describing, Polymorphic, and Parallelizable User-defined Functions. The title of the paper may be large, but so are the advanced analytics possibilities created by the invention of SQL/MR!

We developed SQL/MR because we saw a growing gap between the deep analytics and application needs of very large data and the capabilities provided by SQL and traditional relational-only data processing. We call this gap, the “SQL Gap.”

SQL and the relational query processing model are well suited for many, but not all data processing tasks. Some queries are cumbersome, non-intuitive or impossible to express in SQL (note: now that SQL is turing complete, nothing is strictly impossible, but it can be very painful and perform very badly) - check out our paper for some examples. Moreover, query optimizers have a limited number of algorithms at their disposal to process data, which leads to convoluted data processing in situations where applying a little domain knowledge can yield a much more straightforward algorithm.

We found traditional user-defined functions (UDFs) to fall short in bridging this gap between SQL and the answers to challenging analytic problems that need to be solved. UDFs are often user-unfriendly, inflexible, and not easily parallelized. SQL/MR functions, in contrast, are designed to be easy to develop, easy to install, and easy to use - providing developers and analysts with a powerful tool to tackle the challenges posed by very large data.

To do this, we integrated the MapReduce programming model with SQL. MapReduce is a well known paradigm for parallel, fault-tolerant data processing that allows developers to write procedural code that is then applied to data in parallel. Pure MapReduce, however, misses out on aspects of SQL and relational data processing that are great - such as query optimizations, managed data, and transactions. By integrating SQL and MapReduce on top of Aster nCluster’s hardware management and fault tolerance, we leverage the strengths of each, resulting in a system that is much more powerful than either in isolation.

SQL/MapReduce At VLDB
SQL/MR is much more than a user-defined function. As our paper title states, a SQL/MR function is self-describing, polymorphic, and parallelizable. Let’s explore each of these characteristics and see why we hope researchers at VLDB will be as excited as we are by SQL/MR.

Self-Describing and Polymorphic
The behavior and output characteristics of a SQL/MR function are determined dynamically at query-time instead of statically at install-time as is the case with traditional user-defined functions. These characteristics allow SQL/MR functions to behave much more like general purpose library functions than single-use, specific application user-defined functions. When a SQL/MR function is used in a query, the nCluster query planner negotiates a contract with the SQL/MR function, providing the function’s input schema and optional user-supplied parameters. In return, the SQL/MR function agrees to a contract that specifies its output schema for the duration of the query. This contract negotiation is what makes SQL/MR functions self-describing, and their ability to be invoked on different input with different optional parameters makes them polymorphic as well. To summarize, by invoking a SQL/MR function on different input, with different optional parameters, the SQL/MR function may export a different output schema and perform different computation - the possibilities are entirely up to the developer!

Parallelizable
The SQL/MR programming model, like that of MapReduce, is inherently parallelizable. Developers write procedural code in the language of their choice, but at runtime that code will run in parallel across hundreds of nodes within an nCluster database. Advanced analytics and application code can now be executed in parallel, directly on data stored within nCluster making nCluster an application-friendly, high performance data warehouse and data application system. Advanced analytics capabilities that we have already pushed inside nCluster using SQL/MR include click-stream sessionization, general purpose time series path matching, dynamic and massively parallel data loading from heterogeneous sources, and genetic sequence analysis.

Bridging the Gap
SQL/MapReduce has matured greatly over the past year and is used by our customers in creative ways we never imagined - proof positive that SQL/MapReduce is an effective way to bridge the “SQL Gap” to deeper analytics on very large data. Check out the other application examples and sample code videos on www.asterdata.com, as well as the MapReduce category of this blog

We’d love to hear from you about any type of analysis you’re doing where stand-alone SQL is becoming overly-complex … and please look us up at the VLDB 2009 show if you’re making the trip to Lyon, France!

(Apart from the authors, Brent Chun, Mohit Aron, Abhishek Marwah, Raghu Venkat, Vinay Bondhugula, and Prasan Roy of Aster Data Systems are notable contributors to the overall SQL/MR effort.)