We announced today that we’ve raised another $5.1M as an addendum to our Series B.

Institutional Venture Partners (IVP) led this addendum to bring our total Series B raise to $17.1M. IVP is very clearly the best later-stage VC firm in Silicon Valley, and it is great to have them on our side as we continue to grow our company. IVP has backed such enterprises as Netflix, WebEx, MySQL, Data Domain, Juniper Networks, and Akamai.

Steve Harrick, Partner at IVP, led this investment for IVP. Steve has a great understanding of the technology infrastructure companies poised to lead their markets. He has led IVP’s investments at WebEx and MySQL and was recently recognized by Forbes Magazine’s 2009 Midas List as one of the leading venture investors in the US.

We had met Steve during our rounds of Series B meetings in Q4 2008 and had really liked and respected each other. At that point, we didn’t have space for two venture firms – having decided to raise $12M; we decided to separate, promising to keep in touch for the future.

In Q1 2009, as the economy grew more uncertain, we re-visited our decision and realized that it would be more prudent to pro-actively build a bigger cash reserve to ensure that we did not stumble in our growth, even as the market deteriorates, and a recovery continues to inch away.

Steve was happy to step in and participate in Aster at the same terms as our Series B. We were delighted to have a person of Steve’s caliber participate whole-heartedly in our growth.

Finally, there has to be some weird co-incidence in the manner in which all of our Series A and Series B venture capital firm participants (Sequoia Capital, JAFCO Ventures, and now IVP) have been investors in database companies that have had successful returns (Netezza, Datallegro, MySQL).

We are very excited that OnMedia has just announced Aster as one of the winners of the AlwaysOn OnMedia 100, a listing of the top 100 private, emerging technology companies in the advertising, publishing, marketing, branding and PR spaces. As a technology enabler for many media clients like MySpace, Invite Media, aCerno, Aggregate Knowledge (and others soon to be announced), we understand the pressures that media faces today. Our customers are a great testament to the fact that Aster has the best solution to meet the rapidly changing needs of media, to keep up with the huge amounts of data they are managing for themselves and their end clients. More info on Aster’s win here.

We announced today that we have raised $12M in our Series B funding.

We’re thrilled that our vision and message have resonated so well in these times when investors have become extremely selective. And frankly that is because our message is resonating so well with our customers: within 4 months of publicly launching the company in May 2008, our customer base had grown to double digits with several of our customers deploying/considering a second production system.

The inside story is that we had scheduled our first meetings to raise Series B for the week of October 5. It turned out to be the week of a perfect financial storm. The week began with Iceland’s currency dropping 30% against the Euro. On October 6, several European governments stepped up to guarantee bank deposits. On October 7, as Iceland and Russia exchanged Yes and No statements on a Euro 4B loan, Sequoia Capital gathered its portfolio CEOs in an auditorium and warned them (and by the power and reach of the Web, every venture capitalist, entrepreneur, executive and journalist) that the time had come to “Get Real or Go Home”. By October 10, the cost of short-term credit had spiraled up and as a result DJI dove to 8,451 from 9,955 at the start of the week, its lowest point in the previous 5 years.

Tasso and I still went to our scheduled meetings to present Aster’s opportunity and our customer adoption, and got invited back. In the next 5 weeks, as the financial markets continued their drop off a cliff, we continued to receive strong interest for investing that matured with multiple term-sheets for us to consider.

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When I can afford a multi-week break, I head home to India. It is really impressive how home refreshes and recharges a person as nothing else does.

India has a burdgeoning media and communications industry. The movie industry is well-known and huge, but in truth the story of media in India in the last decade has been that of television coming of age. Internet as a media channel is still in its infancy here. Even in communications, Internet’s growth is dwarfed substantially by the growth of mobile networks.

The power of media in shaping conversations and spreading awareness really hit home in two separate anecdotes.

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We received a very nice Holiday Card today from one of our investors, First Round Capital, that we helped create.

Rob Hayes visited our office in early December with a hand-held video camera asking us to dance! I was quite nervous us filming the video: I had of course seen Matt’s “Where the hell is Matt” video. The untold back-story is that we’re supposed to just start dancing to an imagined tune: there is no-one keeping beats or playing a song!

The end-result is equal parts goofy and equal parts fun! Happy holidays!!

I was at Defrag 2008 yesterday and it was a wonderful, refreshing experience. A diverse group of Web 2.0 veterans and newcomers came together to accelerate the “Aha!” moment in today’s online world. The conference was very well organized and there were interesting conversations on and off the stage.

The key observation was that individuals, groups and organizations are struggling to discover, assemble, organize, act on, and gather feedback from data. Data itself is growing and fragmenting at an exponential pace. We as individuals feel overwhelmed by the slew of data (messages, emails, news, posts) in the microcosm, and we as organizations feel overwhelmed in the macrocosm.

The very real danger is that an individual or organization’s feeling of being constantly overwhelmed could result in the reduction of their “Aha!” moments – our resources will be so focused on merely keeping pace with new information that we won’t have the time or energy to connect the dots.

The goal then is to find tools and best practices to enable the “Aha!” moments – to connect the dots even as information piles up on our fingertips.

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It is really remarkable how many companies today view data analytics as the cornerstone of their businesses.aCerno is an advertising network that uses powerful analytics to predict which advertisements to deliver to which person at what time. Their analytics are performed on completely anonymous consumer shopping data of 140M users obtained from an association of 450+ product manufacturers and multi-channel retailers. There is a strong appetite at aCerno to perform analytics that they have not done before because each 1% uplift in the click-through rates is a significant revenue stream for them and their customers.

Aggregate Knowledge powers a discovery network (The Pique Discovery™ Network) that delivers recommendations of products and content based on what was previously purchased and viewed by an individual using the collective behavior of the crowds that had behaved similarly in the past. Again, each 1% increase of engagement is a significant revenue stream for them and their customers.

ShareThis provides a sharing network via a widget that makes it simple for people to share things they find online with their friends. In a short period of time since their launch, ShareThis has reached over 150M unique monthly users. The amazing insight is that ShareThis knows which content users actually engage with, and want to tell their friends about! And in its sheer genius, ShareThis gives away its service to publishers and consumers free; relying on delivering targeted advertising for its revenue: by delivering relevant ad messages while knowing the characteristics of that thing being shared. Again, the better their analytics, the better their revenue.

Which brings me to my point: data analytics is a direct contributor of revenue gains in these companies. Read the rest of this entry »

There has been a lot of turmoil this past week in Financial Services. Several good people had their projects stalled, or even lost their jobs, due to market forces beyond their control.

I’d like to call out to Quantitative Computer Scientists who have been affected. If you are good with data and know how to extract intelligence from it, we want you in our team!

We are hiring. You’ll have the chance to work with a number of our customers and help them do more with their data. You’ll bring a fresh perspective to the business processes at our customers; in turn, you’ll gain from learning about the business processes of various verticals. An invaluable education when you want to go back to Financial Services after the crisis has passed in a couple of years.

Drop us a note at careers [at] asterdata [dot] com. We’d love to hear from you!

Pardon the tongue-in-cheek analogy to Oldsmobile when describing user-defined functions (UDFs), but I want to draw out some distinctions between this new class of functions that In-Database MapReduce enables.

While similar on the surface, in practice there are stark differences between Aster In-Database MapReduce and traditional UDF’s.

MapReduce is a framework that parallelizes procedural programs to offload traditional cluster programming. UDF’s are simple database functions and while there are some syntactic similarities, that’s where the similarity ends. Several major differences between In-Database MapReduce and traditional UDF’s include:

Performance: UDF’s have limited or no parallelization capabilities in traditional databases (even MPP ones). Even where UDF’s are executed in parallel in an MPP database, they’re limited to accessing local node data, have byzantine memory management requirements, require multiple passes and costly materialization. In constrast, In-Database MapReduce automatically executes SQL/MR functions in parallel across potentially hundreds or even thousands of server nodes in a cluster, all in a single-pass (pipelined) fashion.

Flexibility: UDF’s are not polymorphic. Some variation in input/output schema may be allowed by capabilities like function overloading or permissive data-type handling, but that tends to greatly increase the burden on the programmer to write compliant code. In contrast, In-Database MapReduce MR/SQL functions are evaluated at run-time to offer dynamic type inference, an attribute of polymorphism that offers tremendous adaptive flexibility previously only found in mid-tier object oriented programming.

Manageability: UDF’s are generally not sandboxed in production deployments. Most UDF’s are executed in-process by the core database engine, which means bad UDF code can crash a database. SQL/MR functions execute in their own process for full fault isolation (bad SQL/MR code results in an aborted query, leaving other jobs uncompromised). A strong process management framework also ensures proper resource management for consistent performance and progress visibility.

I’m unbelievably excited about our new In-Database MapReduce feature!

Google has used MapReduce and GFS on page rank analysis, but the sky is really the limit for anyone to build powerful analytic apps. Curt Monash has posted an excellent compendium of applications that are successfully leveraging the MapReduce paradigm today.

A few examples of SQL/MapReduce functions that we’ve collaborated with our customers on so far:

1. Path Sequencing: SQL/MR functions can be used for developing regular expression matching of complex path sequences (eg. time series financial analysis or clickstream behavioral recommendations). It can also be extended to discover Golden Paths to reveal interesting behavioural patterns useful for segmentation, issue resolution, and risk optimization.

2. Graph Analysis: many interesting graph problems like BFS (breadth first search), SSSP (single source shortest path), APSP (all-pairs shortest path), and page rank that depend on graph traversal.

3. Machine Learning: several statistical algorithms like linear regression, clustering, collaborative filtering, naive bayes, support vector machine, and neural networks can be used to solve hard problems like pattern recognition, recommendations/market basket analysis, and classification/segmentation.

4. Data Transformations and Preparation: Large-scale transformations can be parameterized as SQL/MR functions for data cleansing and standardization, unleashing the true potential for Extract-Load-Transform pipelines and making large-scale data model normalization feasible. Push down also enables rapid discovery and data pre-processing to create analytical data sets used for advanced analytics such as SAS and SPSS.

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