Tag: API

Feed: CDH – Cloudera Engineering Blog. Author: Cy Jervis. Analytical and operational access patterns are very different and until now the Hadoop ecosystem has not had a single storage engine that could support both. As a result, engineers have been forced to implement complex architectures that stitch multiple systems together in order to provide these capabilities. On one hand immutable data on HDFS offers superior analytic performance, while mutable data in Apache HBase is best for operational workloads. Apache Kudu bridges this gap.   Kudu’s architecture is shaped towards the ability to provide very good analytical performance, while at the ... Read More

Feed: CDH – Cloudera Engineering Blog. Author: Justin Kestelyn. Solr 5 includes a completely re-written faceted search and analytics module with a structured JSON API to control the faceting and analytics commands. Here’s how it works. Since I joined Cloudera a few years ago to help bring search-powered analytics to Cloudera’s platform, I’ve been working actively upstream alongside the rest of the Solr community to develop new functionality that will drive more interesting applications on Cloudera Search (which is based on an integration of Solr with the Apache Hadoop ecosystem). In the following re-post from my personal blog, I describe ... Read More

Feed: All - O'Reilly Media. Author: Jesse Anderson. Oranges and apples (source: Ruth Hartnup on Flickr) Check out Jesse Anderson’s tutorial “Real-time systems with Spark Streaming and Kafka” at the Strata Data Conference, New York City, Sept. 25-28, 2017. Best price ends August 11. If you look up the phrase “boiling the ocean,” it’s defined as writing a post on choosing a cloud provider—there are so many different facets and use cases, and each variable complicates your choice. The key is to narrow the field to your specific situation and needs. In this article, I share some of the early ... Read More

Feed: All - O'Reilly Media. Author: Ben Lorica. Stockton Infinity Bridge (source: darren price on Flickr) There will back-to-back half-day tutorials on reinforcement learning at the upcoming Artificial Intelligence Conference in San Francisco: Building reinforcement learning applications with Ray and Introduction to reinforcement learning. As more companies begin to experiment with and deploy machine learning in different settings, it’s good to look ahead at what future systems might look like. Today, the typical sequence is to gather data, learn some underlying structure, and deploy an algorithm that systematically captures what you’ve learned. Gathering, preparing, and enriching the right data—particularly training ... Read More

Feed: All - O'Reilly Media. Author: Andrew Odewahn. JupyterHub + Kubernetes (source: O'Reilly) Meet the team behind JupyterHub at JupyterCon on August 22-25, 2017, in New York City. Registration is now open. JupyterHub, a "multi-user server for Jupyter Notebooks," is an essential tool for teaching and training at scale with Jupyter. As described in The course of the future – and the technology behind it , JupyterHub is being used to power an introductory class in data science taken by hundreds of students at Berkeley every semester. JupyterHub is a complex piece of software, and setting up and operating has been ... Read More

Feed: All - O'Reilly Media. Author: Mary Treseler. Distribution Center (source: GRAPHICALBRAIN) ​This post is part of a series of Q&A interviews going behind the scenes with speakers from our October 2017 Velocity Conference in New York. Register for the O’Reilly Velocity Conference to join Joseph Breuer, Robert Reta, and other industry experts. Use code ORM20 to save 20% on your conference pass (Gold, Silver, and Bronze passes). I recently asked Joseph Breuer and Robert Reta, both Senior Software Engineers at Netflix, to discuss what they have learned through implementing a service at scale at Netflix. Joseph and Robert will ... Read More

Feed: All - O'Reilly Media. Author: Manu Jeevan, Suresh Chelladurai. Road signs (source: Pixabay) The full Jupyter Notebook for this post can be accessed here. Although there are many deep learning frameworks, including TensorFlow, Keras, Torch, and Caffe, Apache MXNet in particular is gaining popularity due to its scalability across multiple GPUs. In this blog post, we'll tackle a computer vision problem: classifying German traffic signs using a convolutional neural network. The network takes a color photo containing a traffic sign image as input, and tries to identify the type of sign. The full notebook can be found here In ... Read More

Feed: R-bloggers. Author: R Views. Editor’s Note: The 2017 Chapman University DataFest was held during the weekend of April 21-23. The 2018 DataFest will be held during the weekend of April 27-29. DataFest was founded by Rob Gould in 2011 at UCLA with 40 students. In just seven years, it has grown to 31 sites in three countries. Have a look at Mine Çetinkaya-Rundel’s post Growth of DataFest over the years for the details. In recent years, it has been difficult for UCLA to keep up with the growing interest and demand from southern California universities. This year, the Chapman ... Read More

Feed: Planet MySQL; Author: Keith Hollman; I’ve had some questions from people using MySQL Cluster GPL and wanting to move to using MySQL Cluster Carrier Grade Edition, i.e., they want to use MySQL Cluster Manager, MCM, to make their lives much easier, in particular, upgrading (as well as config change ease and backup history). All I want to do here is to share with you my personal experience on migrating what’s considered a ‘wild’ NDB Cluster to a MCM managed cluster. It’s just as simple to follow the manual chapter Importing a Cluster into MySQL Cluster Manager so at least ... Read More

Feed: Google Cloud Platform Blog. Author: GCP Team. By Brad Svee, Staff Cloud Solutions Architect With all the buzz around Machine Learning as of late, it’s no surprise that companies are starting to experiment with their own ML models, and a lot of them are choosing TensorFlow. Because TensorFlow is open source, you can run it locally to quickly create prototypes and deploy fail-fast experiments that help you get your proof-of-concept working at a small scale. Then, when you’re ready, you can take TensorFlow, your data, and the same code and push it up into Google Cloud to take advantage ... Read More