big data

The author of Appcelerator Titanium: Up and Running describes how Titanium can be used to generate native mobile apps from JavaScript code. He distinguishes the Titanium platform from native API programming and from other popular JavaScript platforms for mobile devices. We look at the way Titanium exploits the expressiveness and flexibility of JavaScript, and some of the directions that the Appcelerator company is taking Titanium."

Building native apps from JavaScript using Titanium - John Anderson

O'Reilly's Ben Lorica chats with Michael Stack to get an update on the Apache HBase project.

Coming Full Circle With Bigtable and HBase

O'Reilly's Ben Lorica chats with Anima Anandkumar about tensor decomposition techniques for machine learning.

The Tensor Renaissance In Data Science

O'Reilly's Jenn Webb chats with Filament co-founder and CEO Eric Jennings about how a decentralized Internet would work, what we need to do to get there, and why it will become necessary as the Internet of Things begins to scale. Eric also talks about why his company recently shifted its focus from the maker community to industrial manufacturing.

A Vision of a Decentralized IoT Stack

O'Reilly's Mary Treseler talks with information architect Jorge Arango about the state of IA and the importance of designers' understanding of context and perspective.

Information Architecture's Role in UX Design

O'Reilly's Mac Slocum chats with Rachel Andrew, founder of edgeofmyseat.com, about CSS Grid Layout and the role responsive design is playing in emerging Web technologies. Slocum also chats with open Web evangelist Kyle Simpson, who defends JavaScript Coercion’s negative reputation and contemplates the past, present, and future of the Web.

CSS Grid Layout: The Modern Way of Doing Layout on the Web

O'Reilly's Mary Treseler chats with PARC's Mike Kuniavsky about PARC’s work on IoT and the mindset shift the IoT will require.

Designing for Ecosystems: Making Meaningful IoT Products and Services

O'Reilly's Ben Lorica chats with Gary Kazantsev, who runs the R&D Machine Learning group at Bloomberg LP, about how big data and data science are making a difference in finance.

Data Science Makes an Impact on Wall Street

O'Reilly's Ben Lorica chats with Patrick Wendell, release manager of Apache Spark and co-founder of Databricks, about how he came to join the UC Berkeley AMPLab, the current state of Spark ecosystem components, Spark’s future roadmap, and interesting applications built on top of Spark.

Apache Spark: Powering Applications On-Premise and in the Cloud

O'Reilly's Mike Hendrickson chats with database pioneer and 2014 Turing Award recipient Michael Stonebraker about winning the award, the future of data science, and the importance — and difficulty — of data curation.

The Future of Data at Scale

O'Reilly's Ben Lorica chats with Ihab Ilyas, professor at the University of Waterloo and co-founder of Tamr, about how he started working on data cleaning tools, academic database research, and training computer science students for positions in industry.

Why Data Preparation Frameworks Rely on Human-in-the-Loop Systems

Rajiv Maheswaran on the science of moving dots, and Claudia Perlich on big data in advertising.

Movement Data is Going to Transform Everything

Evan Chan, distinguished engineer at Tuplejump, on the early days of Spark (particularly his contributions to Spark/Cassandra integration), his interesting new open source project (FiloDB), and recent trends in cloud computing.

Building a Scalable Platform for Streaming Updates and Analytics

In this episode of the O’Reilly Data Show, I spoke with Fang Yu, co-founder and CTO of DataVisor. We discussed her days as a researcher at Microsoft, the application of data science and distributed computing to security, and hiring and training data scientists and engineers for the security domain.

Using Apache Spark to Predict Attack Vectors Among Billions of Users and Trillions of Events

Ben Lorica chats with Teradata’s Sri Raghavan about the evolution of data analytics in Spark. They discuss why some feel there is still a high barrier for entry with Spark for data analytics, and some tools to help overcome this barrier.

The evolution of advanced analytics in Spark

In this episode of the Data Show, I spoke with Ameet Talwalkar, assistant professor of machine learning at CMU and co-founder of Determined AI. He was an early and key contributor to Spark MLlib and a member of AMPLab. Most recently, he helped conceive and organize the first edition of SysML, a new academic conference at the intersection of systems and machine learning (ML).

We discussed using and deploying deep learning at scale. This is an empirical era for machine learning, and, as I noted in an earlier article, as successful as deep learning has been, our level of understanding of why it works so well is still lacking. In practice, machine learning engineers need to explore and experiment using different architectures and hyperparameters before they settle on a model that works for their specific use case. Training a single model usually involves big (labeled) data and big models; as such, exploring the space of possible model architectures and parameters can take days, weeks, or even months. Talwalkar has spent the last few years grappling with this problem as an academic researcher and as an entrepreneur. In this episode, he describes some of his related work on hyperparameter tuning, systems, and more.

big data

H L Drew