Sumo Logic, the “big data” startup that focuses on analytics, has pulled in $30 million for its third-round of funding.

Most large companies will generate terabytes of log data every day. This data can be used to troubleshoot operational issues, uncover security risks and ensure regulatory compliance.

Sumo Logic wants to replace company’s traditional log management systems, which typically leave 98% of data unanalyzed. Its founders say that Sumo Logic is unique as it’s cloud-based and can be set up in a matter of minutes.

The Silicon Valley-based startup, which competes most closely with Splunk, has scored some major-league enterprise customers that are willing to pay for its “premium” offering. The startup also offers a free service that is popular with startups and small businesses.

According to the company’s CEO, Vance Loiselle, Sumo Logic has signed on almost 40 paying customers since launching this February. It is used by the likes of GoDaddy, Netflix, Ooyala and Formspring to turn their log management data into actionable insights.

“Our log reduce engine can take thousands of pages of results and then shrink it down into logical patters based on our machine learning algorithms,” said Loiselle. “That’s a better way to get direction and insight than sifting through thousands of results.”

With the funding, the company will build out its sales, marketing and operations team.

Accel Partners, a Silicon Valley venture capital firm with a $100 million fund dedicated to big data startups, will lead the financing. Existing investors Sutter Hill Ventures and Greylock Ventures also participated in the round.

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Elasticsearch, the open source project for developers to slice and dice their data, has pulled in $10 million.

Since launching in 2009, Amsterdam-based Elasticsearch has become one the most popular open source frameworks on the market — the company told me it has been downloaded 1.5 million times — it experienced 185,000 downloads in September and 200,000 in the month of October alone.

Shay Banon, cofounder and CTO, claims that the company is differentiated for it’s “general purpose big data” solution — it’s not just an enterprise search tool to index structured data. “People are using it to store huge amounts of logs and drive analytics,” he said.

Organizations are struggling to draw business insights from mountains of unstructured (texts, emails, and so on) and structured data. Elasticsearch and the growing crop of well-funded “big data” startups, will claim to provide the simplest way to crawl through petabytes of information, store a massive volume of data, and extract the most relevant information.

To avoid being delineated as search solution for companies of all sizes (the term “enterprise search” is somewhat outdated and is most often associated with legacy vendors like HP’s Autonomy and Oracle-owned Endeca) it is branding itself as a big data company.

To get ahead of the trend, the product was designed to be robust and easy to use; “it has an extremely simple API,” Banon explained on a phone interview with VentureBeat. “You don’t have to be a data scientist to use it — the analytics are useful to marketing, business and customer support teams.”

According to Banon, the major challenge has been to ensure that “business execution catches up with the adoption rate of the open source project.” In other words, it’s a firm favorite with devs, but it’s time for the founding team to double-down on a revenue model. To be closer to the action and to their investors, he revealed that they are actively looking to relocate to San Francisco.

What’s interesting is that this is the only legitimate competitor that has emerged to give Apache Lucene / Solr (the standalone open-source enterprise search project) a run for its money. Solr was invented in 2004 at CNET Networks, joined with Lucene, and is currently run by the Apache Software Foundation’s dev team.

Shay Banon, Elasticsearch’s CTO

Likewise, Elasticsearch’s business model takes a page from LucidWorks’s book. LucidWorks is the commercial arm of Apache Lucene / Solr, and makes its money by selling support services. Elasticsearch offers production support subscriptions and training courses to its customers.

Despite the competition, Benchmark Capital were impressed by the company’s sky-rocketing growth, and drew a comparison to previous open source investments, RedHat, MySQL and SpringSource. “Shay’s [Banon] magnetic energy in delivering a simple, and highly-usable solution for extracting meaning and information from big data sets the company apart from the current market hype and hyperbole,” said Peter Fenton, general partner, Benchmark Capital in a statement.

This funding round was led by Benchmark Capital with participation Rod Johnson, cofounder of SpringSource, and from big data focused venture fund, Data Collective.

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Skytree, a “big data” company that came out of stealth mode in February, has filed for additional funding from existing investors, Javelin Venture Partners.

The startup has a tool to bring machine learning techniques to a mainstream market and has raised $1 million of a $2 million round, according to the security and exchange commission (SEC).

Machine learning lets systems get smarter over time, which helps data scientists and developers uncover patterns in a chaotic mess of data. Skytree’s eponymous product is known as “Skytree Server.”

The company has hired a team of machine-learning Ph.D.s to design the server and integrate with existing data stores, including Hadoop. The hope is that it will process and query data 10 times faster than is currently possible.

The startup is interesting to investors as it has taken on a meaty technical challenge. Machine learning is a particularly complex approach to big data that has not been fully leveraged. Only a handful of tech companies, government agencies, and financial services firms have integrated machine learning.

The San Jose-based company raised its series A round from Javelin Venture Partners in February, 2012. Read the SEC filing.

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RainStor is the second startup today to announce a multimillion-dollar fundraise in the “big data” market (Trifacta, which just raised $4.3M, is the other).

San Francisco-based RainStor pulled in $12 million today for relational database products that allow new ways to store large amounts of data.

Companies of all sizes are increasingly looking to exploit big data — vast stores of both unstructured (texts, emails, reports, and so on) and structured data – for business insights. As a result, there is no shortage in venture capital funding for big data startups that have the security and scalability required to sell their product to large enterprises.

RainStor runs on the Hadoop Distributed File System, which means it can scale out its data storage and analytics capabilities, potentially to multi-perabyte data volumes. According to the company, its technology is easier to use than most and doesn’t require a staff member to have specialized database administrator (DBA) skills.

They call it “NewSQL,” as it’s a novel approach to a decades-old technology and is designed to run on-premise or in the cloud. What’s interesting here is that most startups in this space have heralded the death of the relational database.

The startup will use the funding to step up its sales and marketing efforts. To succeed, it will need to win customers from competitors in the big data space, which include traditional players like EMC, and startups such as CopperEye.

RainStor’s third round was led by Credit Suisse and Rogers Venture Partners, with participation by existing investors Doughty Hanson Technology Ventures, Storm Ventures and The Dow Chemical Company. This most recent round brings the company’s total funding to $23.5 million.

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