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This is a guest post written by Claude Johnson, a Lead Site Reliability Engineer at salesforce.com.

The following is an architectural overview of salesforce.com’s core platform and applications. Other systems such as Heroku's Dyno architecture or the subsystems of other products such as work.com and do.com are specifically not covered by this material, although database.com is. The idea is to share with the technology community some insight about how salesforce.com does what it does. Any mistakes or omissions are mine.

This is by no means comprehensive but if there is interest, the author would be happy to tackle other areas of how salesforce.com works. Salesforce.com is interested in being more open with the technology communities that we have not previously interacted with. Here’s to the start of “Opening the Kimono” about how we work.

Since 1999, salesforce.com has been singularly focused on building technologies for business that are delivered over the Internet, displacing traditional enterprise software. Our customers pay via monthly subscription to access our services anywhere, anytime through a web browser. We hope this exploration of the core salesforce.com architecture will be the first of many contributions to the community.

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 AlbumIn the business of selling stuff, there’s a lot of managing. Sales reps usually have a boss they check in with on the status of deals in the pipeline, maybe to get some advice on how to close a deal when there’s stiff competition from another company, or to go over how an important customer was reeled in, so that others can learn from it.

These check-ins are sometimes referred to as coaching, and there is data to show that coaching can boost sales performance. A study by the Sales Executive Council suggests that reps who received three or more hours of coaching per month outsold those who received two hours or less of coaching per month, by as much as 17 percent.

Getting that coaching done can be kind of a hassle. But it’s the sort of hassle that Salesforce.com has often sought to understand intimately, and then create products within its suite of cloud software tools.

Today is one of those days. The company is announcing a trial of a new feature that closely ties its traditional Sales Cloud with its Work.com product. The point is to do a few things: Speed up the review portion that has always tended to be a big consumer of time and attention in pretty much any organization, and also to make it easier for sales managers to find ways to motivate their teams to, you know, sell more stuff, which is basically the point of sales in the first place.

Through a combination of Salesforce services including the Sales Cloud, its social enterprise platform Chatter and Work.com, an HR software outfit that includes the Rypple acquisition it made last year, sales teams will see each other’s goals, will learn about big deals coming in, and know about each other’s expertise.

The new tools will also give managers a way to provide instant feedback and public recognition to those sales people who are doing well. Remember “gamification”? It’s not my favorite word, but apparently it works to some extent, especially with sales people who have monthly, quarterly and annual targets to make.

There is research to back up the assertion that when people leave sales jobs they do so in part because they don’t think they’re getting enough recognition from above. Now, on those occasions when a rep lands a big customer in a competitive deal, the manager can publicly pat them on the back with a “thanks in Chatter” feature, and give them a “sales Ninja” badge, or something like it, that everyone can see in their Chatter feeds.

Think it all sounds hokey? Maybe it is, but there’s a lot of evidence that these things have a way to making sales people happier on the job. And happy sales reps are sales reps who close deals, or least that’s the theory. We’ve come a long way since Alec Baldwin’s memorable (and profanity-laced) monologue in “Glengarry Glen Ross.”

The new features are coming in early 2013, and are available for certain Salesforce customers on a pilot basis starting today.

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hal380The advent of Salesforce Marketing Cloud and Adobe Marketing Cloud demonstrates the need for enterprises to develop new ways of harnessing the vast potential of big data. Yet these marketing clouds beg the question of who will help marketers, the frontline of businesses, maximize marketing spending and ROI and help their brands win in the end. Simply moving software from onsite to hosted servers does not change the capabilities marketers require — real competitive advantage stems from intelligent use of big data.

Marc Benioff, who is famous for declaring that “Software Is Dead,” may face a similar fate with his recent bets on Buddy Media and Radian6. These applications provide data to people who must then analyze, prioritize and act — often at a pace much slower than the digital world. Data, content and platform insights are too massive for mere mortals to handle without costing a fortune. Solutions that leverage big data are poised to win — freeing up people to do the strategy and content creation that is best done by humans, not machines.

Big data is too big for humans to work with, at least in the all-important analytical construct of responding to opportunities in real time — formulating efficient and timely responses to opportunities generated from your marketing cloud, or pursuing the never-ending quest for perfecting search engine optimization (SEO) and search engine marketing (SEM). The volume, velocity and veracity of raw, unstructured data is overwhelming. Big data pioneers such as Facebook and eBay have moved to massive Hadoop clusters to process their petabytes of information.

In recent years, we’ve gone from analyzing megabytes of data to working with gigabytes, and then terabytes, and then petabytes and exabytes, and beyond. Two years ago, James Rogers, writing in The Street, wrote: “It’s estimated that 1 Petabyte is equal to 20 million four-door filing cabinets full of text.” We’ve become jaded to seeing such figures. But 20 million filing cabinets? If those filing cabinets were a standard 15 inches wide, you could line them up, side by side, all the way from Seattle to New York — and back again. One would need a lot of coffee to peruse so much information, one cabinet at a time. And, a lot of marketing staff.

Of course, we have computers that do the perusing for us, but as big data gets bigger, and as analysts, marketers and others seek to do more with the massive intelligence that can be pulled from big data, we risk running into a human bottleneck. Just how much can one person — or a cubicle farm of persons — accomplish in a timely manner from the dashboard of their marketing cloud? While marketing clouds do a fine job of gathering data, it still comes down to expecting analysts and marketers to interpret and act on it — often with data that has gone out of date by the time they work with it.

Hence, big data solutions leveraging machine learning, language models and prediction, in the form of self-learning solutions that go from using algorithms for harvesting information from big data, to using algorithms to initiate actions based on the data.

Yes, this may sound a bit frightful: Removing the human from the loop. Marketers indeed need to automate some decision-making. But the human touch will still be there, doing what only people can do — creating great content that evokes emotions from consumers — and then monitoring and fine-tuning the overall performance of a system designed to take actions on the basis of big data.

This isn’t a radical idea. Programmed trading algorithms already drive significant activity across stock markets. And, of course, Amazon, eBay and Facebook have become generators of — and consummate users of — big data. Others are jumping on the bandwagon as well. RocketFuel uses big data about consumers, sites, ads and prior ad performance to optimize display advertising. Turn.com uses big data from consumer Web behavior, on-site behaviors and publisher content to create, optimize and buy advertising across the Web for display advertisers.

The big data revolution is just beginning as it moves beyond analytics. If we were building CRM again, we wouldn’t just track sales-force productivity; we’d recommend how you’re doing versus your competitors based on data across the industry. If we were building marketing automation software, we wouldn’t just capture and nurture leads generated by our clients, we’d find and attract more leads for them from across the Web. If we were building a financial application, it wouldn’t just track the financials of your company, it would compare them to public filings in your category so you could benchmark yourself and act on best practices.

Benioff is correct that there’s an undeniable trend that most marketing budgets today are betting more on social and mobile. The ability to manage social, mobile and Web analysis for better marketing has quickly become a real focus — and a big data marketing cloud is needed to do it. However, the real value and ROI comes from the ability to turn big data analysis into action, automatically. There’s clearly big value in big data, but it’s not cost-effective for any company to interpret and act on it before the trend changes or is over. Some reports find that 70 percent of marketers are concerned with making sense of the data and more than 91 percent are concerned with extracting marketing ROI from it. Incorporating big data technologies that create action means that your organization’s marketing can get smarter even while you sleep.

Raj De Datta founded BloomReach with 10 years of enterprise and entrepreneurial experience behind him. Most recently, he was an Entrepreneur-In-Residence at Mohr-Davidow Ventures. Previously, he was a Director of Product Marketing at Cisco. Raj also worked in technology investment banking at Lazard Freres. He holds a BSE in Electrical Engineering from Princeton and an MBA from Harvard Business School.

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Steve Anderson's first Instagram photo

Steve Anderson of Baseline Ventures has had an amazing run lately.

He was the first seed investor in Instagram, which sold to Facebook yesterday for about $1 billion. He was also a seed investor in OMGPOP, which sold to Zynga for close to $200 million last month, and Heroku, which Salesforce picked up for about $200 million in 2012.

He's also got early investments in Twitter and Path, which are sure to be two of the biggest success stories of the next couple years.

So how does he pick them?

In the case of Instagram, he met founder Kevin Systrom in January 2010 when Systrom was still at Google. They met at a bar, introduced through another entrepreneur the two of them knew.

Anderson told us, "He had his iPhone and bunch of HTML code he'd hacked together called Brbn. It was a bunch of hypotheses and no real clear answer what to do -- photos, check-in, comments, gamification. Instagram emerged out of that, a paring down of features that were really working."

Through trial and error they quickly decided "that HTML5 wasn't really ready for prime time, that native apps were totally superior." They also decided to "make a sole bet on the iPhone, which was risky at the time."

It paid off -- Instagram got 25,000 users on the first day, showing obvious product market fit. A little more than year later, it was up to 30 million.

The rest is history.

Anderson honed his investing skills working at Kleiner Perkins earlier this decade, and also did stints at eBay in the early days, and on Microsoft's Windows Server team.

We caught up with Anderson this morning. He shared some of his wisdom with us:

  • He wants founders who are thoughtful, but then make forceful decisions. This is important because few people get their idea exactly right on the first try. "As Instagram has proven with Brbn to Instagram, I find it really hard to believe that any one person will have the perfect maniacal vision right from the start .... So that's part of my assessment, hey, does this person have an opinion, based on a worldview that they've developed through listening, paying attention, and pondering. Kevin is the best at that. He's very thoughtful, he ponders, but he's very deliberate once he makes a decision."
  • Founders must be able to code, but he doesn't have strong opinions about languages. "All the scripting languages -- Ruby, Python, NoJS -- those have all very important places as do people who know Objective C for iOS development. Even C++. You don't use it to develop a really cool app like Instagram, but you need C++ if you want to do anything fast tied to hardware, or anything operating-system based."
  • Don't raise more money until you're sure you have product market fit. "What are the goals for [follow-on rounds] of financing? In Kevin's case...the goal was to take all these features [in Brbn] and narrow them to a product that has a great product market fit. Generally speaking, most of my investments are pre product launch, they're just an idea. So getting product market fit is the most important goal of the round. My goal as an investor is to make sure there's enough financing to give companies time to do that, a year to 18 months. The worst scenario is to try to raise more money when you haven't achieved that goal."
  • To be a great seed investor, you need a great network of connections. "As a seed investor, I don't what exists until I find it. With series A, B, C, or growth investments, you already know what you want to invest in. You have to fight for it, convince the team that it's the right investment at the right time and right price. But for me, it's all about networks ... I spend time with entrepreneurs, I meet them mostly through other entrepreneurs."
  • On Y Combinator and other accelerators and incubators. "They did this for people who don't have their own networks or can't grow their networks. How often do you show up to one place and see 80 companies? Of course with that scenario you'll pay a higher price because more people are looking. That's fine. Entrepreneurs have more transparency today than ever before, they can choose the types of investors they want to work with."
  • Anderson is fine with all the other seed and angel investors who have sprung up since he started. "When I went out at Baseline to raise capital, all I heard was crickets. I had to stay the course, adjust, and scrape together what I could. Six years later, there are a lot of investors ... I don't worry, I'm very confident in my ability to add value to young entrepreneurs and young companies. I like working with other investors. It's a big market, it's less about seeing everything and more about seeing your fair amount so you can be able to pick."
  • When to shut down a failing company. "Your goal is product market fit. If you don't have it, eventually you'll run out of cash, say the experiment is wrong, and fold up your tent ... A lot comes down to the entrepreneur. Do you keep doing this against all the feedback, or not? That's why when I invest I want to leave enough room for pivoting or reexamining your goals. After that, most of the time entrepreneurs are realistic near the end and say this isn't working. Those decisions aren't that difficult. It gets more difficult in later stages when you've got millions of dollars in. Usually there, you try to sell the company."

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healthy_market

Editor’s Note: This guest post is written by Doug Pepper, who is a General Partner at InterWest Partners where he invests in SaaS, mobile, consumer Internet and digital media companies. He blogs at dougpepper.blogspot.com.

Everyone expects startups, even successful ones, to undergo a cycle of hype, disappointment and ultimately growth on the way to a sustainable business. But what about new technology markets themselves? Does the growth of a new market follow a similar pattern?

Fred Wilson recently wrote about the twists and turns that startups face (expanding on Paul Graham’s astute “Startup Curve”). I’d like to take those ideas further and describe the “Market Curve” — a similar path that new markets take on the path to sustainability.

The chart below shows the basic pattern. Markets often experience a “Hype Cycle” of overheated expectations followed by a trough — call it “Facing Reality.” If the market ultimately succeeds, the next phase is “Liftoff.” But troughs don’t end until several ingredients are present. First, there must be broad adoption of core underlying technologies that support the market. Second, there needs to be compelling reference applications to drive mainstream adoption. Finally, there must be a pioneering company, typically with a charismatic leader, that leads the market out of the trough. Obviously not all markets are destined to make it out of their trough.

For entrepreneurs and investors the most exciting element of the Market Curve is that, once the trough ends, strong technology markets ultimately prove more valuable than anyone imagined even during the Hype Cycle. Here are a few examples of how different technology markets fit into this curve.

Internet: Broadband Penetration and YouTube

The late ‘90s saw extreme hype surrounding the Internet but the market was simply not yet ready to deliver. With only five million fixed broadband connections in 2000 the underlying technology wasn’t there. Plus there were very few truly compelling applications. The Internet entered its “Facing Reality” trough in the early 2000’s and failed to live up to initial expectations.

But, by 2005, there were 43 million U.S. broadband connections and addictive applications like YouTube and eventually Facebook. That year Jeff Bezos launched Amazon Prime and convinced mainstream consumers that they could conveniently and safely shop for anything online. Since then, the Internet has proven to be more transformative to our civilization and more ingrained into mainstream culture than ever imagined.

Amazon has surfed the wave of the Internet’s Market Curve almost from the very beginning. Their stock price clearly follows this pattern.

Mobile: The iPhone and App Store

Between 2000 and 2005, nearly every VC firm had Mobile as a core investment sector. And, with few exceptions, those investments were unsuccessful. During that time, mobile networks were slow and unreliable (remember the CDPD network?), devices were clunky and carriers thwarted innovation. Clearly, that all changed when Steve Jobs launched the iPhone in 2007 and replaced the carrier decks with the App Store. And, with more than one billion mobile broadband subscribers globally, the post-PC mobile computing industry is in a “Liftoff” phase that is accelerating beyond wildest expectations.

SaaS: Salesforce.com and Successfactors

When I first joined my VC firm, InterWest Partners, in September 2000, the Application Service Provider (ASP) concept was all the rage. These ASPs offered off-the-shelf software to enterprises delivered over the Internet. However, between 2001 and 2007, adoption was slow because enterprises were more concerned with security risks than the benefits of hosted software.

Over time, Internet security and reliability improved and several pioneering companies, including Marc Benioff’s Salesforce.com and Lars Daalgard’s Successfactors, emerged with proprietary software applications that proved the benefits of SaaS delivery. Today, this market has broadened into a larger paradigm called Cloud Computing with corporations shifting nearly every aspect of their IT infrastructure into the Cloud. This could not have been imagined during the Hype Cycle of this market.

Market Failures: Troughs That Never End

Of course, not every market recovers from its trough. For example, while there are certainly specific nano technologies that are fundamental to many products, a broader nanotechnology market hasn’t emerged. It’s not clear that it ever will. And, in my opinion, Cleantech currently sits at the bottom of the trough. Because of extreme capital intensity, long sales cycles and wavering enterprise and consumer interest in “Green,” this market has become challenged. The question is whether Cleantech will ever emerge from the depths of the trough where it sits today and become the powerful market that John Doerr, Vinod Khosla and many others had hoped.

In the chart below, I show where a number of current technology Markets sit along the Market Curve.

Takeway: Have Conviction During the Trough

The best investors recognize and take advantage of these troughs and the best entrepreneurs lead Markets out of the trough. When SaaS was in the trough, Marc Benioff built Salesforce.com and Dave Strohm invested in Lars Daalgard at Successfactors. When the Internet was in the trough, Jeff Bezos built Amazon.com and Roelof Botha invested in YouTube. In the case of Steve Jobs, he invented a product and pioneered a business model that altered the Mobile market and led it out of the trough. The key is to have conviction about a Market and, as an investor, look for the technologies, products and leaders that will end the trough. Or, as an entrepreneur, launch market leading products and business models to end it yourself.

Marketo is an example of an investment my firm, InterWest, made during a trough. During the late 1990′s, there was a peak of excitement around Marketing Automation with companies like Annuncio, Rubric, Marketfirst and ePiphany. But, the market was not ready. Marketers were not adopting Internet techniques for acquiring customers and they didn’t have sufficient budgets to adopt and implement enterprise software.

By 2006 when InterWest invested in Marketo, the company’s founders believed, and my colleague Bruce Cleveland and I agreed, that the market had progressed along the Market Curve. Marketers had begun consistently utilizing search engine marketing, landing pages, email marketing, and online content marketing … all the activities that are harnessed and optimized by Marketing Automation and Lead Nurturing products. And, the SaaS delivery and business model meant that marketers could quickly see ROI without big budgets or IT resources.

We had conviction that that the Marketo team would create the compelling products needed to lead the Marketing Automation market out of the trough. Today it seems clear that this market will be larger than expected even during the initial Hype Cycle.

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GTAC 2011: Lightning Talks I

6th Annual Google Test Automation Conference 2011 (GTAC 2011) "Cloudy With A Chance Of Tests" Computer History Museum Mountain View, CA USA October 26-27, 2011 Lightning Talks I: A series of 5-minute technical talks about cloud testing. More information about talks and speakers here: www.gtac.biz Testing Cloud Failover Roussi Roussev, VMware Behind Salesforce Cloud: Test Automation Cloud and Yoda Chris Chen, Salesforce ABFT in the Cloud Timothy Crooks, CygNet ScriptCover: Javascript Coverage Analysis Tool Ekaterina Kamenskaya, Google Cloud Sourcing - Realistic Performance, Load and Stress Testing Sai Chintala, AppLabs
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