How generating conversations can become one of the most important data assets for any organization.
At O’Reilly Research, we focus our attention on trends in technology adoption — which tools are adopted and in which industries. In doing so, we uncover interesting cross-disciplinary opportunities and discover what we can learn from innovations in other fields.
We’ve recently learned about the increasing role of data in the fashion industry, so we set out to uncover some of the players who are making disruptive changes using technology and analytics.
Our team asked Liza Kindred, founder of Third Wave Fashion, and Julie Steele, coauthor of Beautiful Visualization and Designing Data Visualizations, to take a closer look at these developments in their new report, “Fashioning Data: How fashion industry leaders innovate with data and what you can learn from what they know.” We think you’ll find some surprising applications of data and analytics in the fashion industry — applications that are useful regardless of the industry or organization you work within. And, we know we’re just at the beginning of what is likely a growing trend. Read more…
A new survey shows the market is ready for cloud-based big data services.
One night when our son was two years old, he abruptly decided that he didn’t like taking baths. As my wife recalls, he struggled mightily against the ritual of bathing for several months until, suddenly and mysteriously, he decided that he liked bathing again. We’re happy to report that he has managed to stay relatively clean ever since.
When I speak with CIOs and other IT leaders about moving big data operations into the cloud, I am reminded of our son’s unexplained loathing of the bathtub.
Nearly everyone associated with IT understands that most IT operations — including big data analytics — must eventually move into the cloud. The traditional on-premises approaches are simply too costly, and CIOs are under crushing pressure to shift budgetary resources to value-added, customer-facing activities.
For most companies, the writing is already on the wall. The cloud offers greater agility and elasticity, and quicker product development cycles — and can reduce costs. When you add up the benefits, it seems inevitable that the bulk of IT operations will move into the cloud. Nevertheless, the foot-dragging and excuse-making continues. Read more…
Solutions to a number of problems must be found to unlock PAPI value.
In November, the first International Conference on Predictive APIs and Apps will take place in Barcelona, just ahead of Strata Barcelona. This event will bring together those who are building intelligent web services (sometimes called Machine Learning as a Service) with those who would like to use these services to build predictive apps, which, as defined by Forrester, deliver “the right functionality and content at the right time, for the right person, by continuously learning about them and predicting what they’ll need.”
This is a very exciting area. Machine learning of various sorts is revolutionizing many areas of business, and predictive services like the ones at the center of predictive APIs (PAPIs) have the potential to bring these capabilities to an even wider range of applications. I co-founded one of the first companies in this space (acquired by Salesforce in 2012), and I remain optimistic about the future of these efforts. But the field as a whole faces a number of challenges, for which the answers are neither easy nor obvious, that must be addressed before this value can be unlocked.
In the remainder of this post, I’ll enumerate what I see as the most pressing issues. I hope that the speakers and attendees at PAPIs will keep these in mind as they map out the road ahead. Read more…
How NoSQL databases scale vertically and horizontally, and what you should consider when building a DB cluster.
Editor’s note: this post is a follow-up to a recent webcast, “Getting the Most Out of Your NoSQL DB,” by the post author, Alex Bordei.
As product manager for Bigstep’s Full Metal Cloud, I work with a lot of amazing technologies. Most of my work actually involves pushing applications to their limits. My mission is simple: make sure we get the highest performance possible out of each setup we test, then use that knowledge to constantly improve our services.
Here are some of the things I’ve learned along the way about how NoSQL databases scale vertically and horizontally, and what things you should consider when building a DB cluster. Some of these findings can be applied to RDBMS as well, so read on even if you’re still a devoted SQL fan. You might just get up to 60% more performance out of that database soon enough. Read more…
Mark Burgess chats about Promise Theory, and Geoffrey Moore discusses a modern approach to his Crossing the Chasm theory.
As systems become increasingly distributed and complex, it’s more important than ever to find ways to accurately describe and analyze those systems, and to formalize intent behind processes, workflows, and collaboration.
A new partnership between O’Reilly and Databricks offers certification and training in Apache Spark.
Editor’s note: full disclosure — Ben is an advisor to Databricks.
I am pleased to announce a joint program between O’Reilly and Databricks to certify Spark developers. O’Reilly has long been interested in certification, and with this inaugural program, we believe we have the right combination — an ascendant framework and a partnership with the team behind the technology. The founding team of Databricks comprises members of the UC Berkeley AMPLab team that created Spark.
The certification exam will be offered at Strata events, through Databricks’ Spark Summits, and at training workshops run by Databricks and its partner companies. A variety of O’Reilly resources will accompany the certification program, including books, training days, and videos targeted at developers and companies interested in the Apache Spark ecosystem. Read more…