Google X Inspired: 8 Moonshots To Watch

This week in big data we've got an acquisition by Hadoop distributor Cloudera, what Nvidia's CEO thinks of the state of AI, news out of the Adobe Summit 2016, Google's machine learning pitch, and more. Plus, we've got a quick look at a new book about applying statistics and analytics to college basketball.

Let's start with the news from Cloudera. This week the company quietly acquired Sense, a big data cloud platform that lets data scientists collaborate with each other.

"We launched Sense with the mission of helping data scientists and data engineers focus on what's important -- extracting value rather than managing infrastructure," wrote Sense founders Tristan Zajonc and Anand Patil in a blog post. "As the quantity of data explodes and machine learning pushes computing to new limits, this mission has only become more important."

The company founders said that Cloudera shares their vision for a world where data helps solve the world's biggest problems, and that the company will continue to make its customers top priority.  

Adobe Summit 2016

While many know Adobe as the maker of PDF software, the company has also built an empire of Web traffic measurement and analytics tools, too, that have become the backbone of many marketing programs today. This week at its Adobe Summit 2016 the company released a host of updates to its platforms, which amounted to a major upgrade across the board.

Adobe updated its Adobe Exchange, which provides apps and integrations for developers who use it to extend the reach of Marketing Cloud. Plus, the company introduced a new developer portal, Adobe.io, which will enable access to APIs and protocols. Adobe's Marketing Cloud accounted for $1.36 billion in revenue, about one third of the total for Adobe.

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Google And Machine Learning

Meanwhile this week, one of Adobe's Web analytics rivals, Google, was promoting another one of its products and services. The company used its Google Cloud Platform Next 2016 conference to promote another technology -- machine learning. Google announced the alpha release of Cloud Machine Learning, a framework for building and training custom data models using distributed learning algorithms based on TensorFlow.

TensorFlow is the company's machine learning framework that Google recently released to open source. How important is machine learning, according to Google? Here's what the company's VP of enterprise business, Diane Greene, said in a keynote address at the event: "Machine learning is pretty much revolutionizing every industry."

Nvidia CEO Talks AI

Another advanced data and analytics technology, artificial intelligence, was on the agenda recently for Nvidia CEO Jen-Hsun Huang. In a recent interview Huang said 2015 was a big year for AI as it moved into the commercial world.

"It wasn't until the last few years that AI could do things that people can't do," he told Fortune in an interview. "Several milestones were achieved in 2015 in particular that made it possible for us to use it in all kinds of areas."

Huang goes on to talk about the area of AI called "deep learning" and noted that Google, Microsoft, and Facebook are all now using AI.

Galactic Exchange and Big Data

In advance of the Strata + Hadoop event next week in San Jose, Calif., Galactic Exchange emerged from stealth mode to launch initial beta availability of ClusterGX, an open source clustering solution, which it says provides "unprecedented simplicity" of deployment and management of Spark/Hadoop clusters. The technology can be deployed on-premise[s] or in a public cloud and can work on any common operating system including Windows, Mac, Linux or bare metal, according to the company, which issued an announcement last week.

Chasing Perfection

Finally this week as we approach the end of the college basketball tournament that is March Madness, we bring you this review from The Washington Post of a new book that looks at how big data is changing college basketball. In the tradition of Moneyball, Chasing Perfection author Andy Glockner introduces readers to a whole new set of metrics that are being used today to analyze college basketball, from gravity scores, to paint touches, to real plus-minus. If you care about college basketball as much as you care about analytics and data, this one is a layup.