Yahoo! Hack India: Hyderabad 2013 | Intro to RaspberryPi
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This document provides a quick introduction to the Raspberry Pi, including an overview of its components and operating system called Raspbian. Raspbian is an official Linux image based on Debian that can be copied to an SD card and used to power the Raspberry Pi. Additional resources for learning more about the Raspberry Pi and programming its GPIO pins are also listed.
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Explore career opportunities at Oath: https://www.oath.com/careers/search-jobs/.Moving the Oath Grid to Docker, Eric Badger, Oath
Presented at the Hadoop Contributors Meetup, hosted by Oath.
Explore career opportunities at Oath: https://www.oath.com/careers/search-jobs/.
Architecting Petabyte Scale AI Applications
Orion is a petabyte scale AI platform developed by the Big Data and Insights (BDAI) team at Oath to generate actionable insights from large datasets through scalable machine learning. The platform can process over 60 billion records per day from a variety of data sources and uses techniques like anomaly detection and predictive algorithms to provide insights that improve efficiencies, reduce costs, and enhance customer experiences. Orion offers a centralized architecture and suite of APIs to build custom solutions for applications in advertising, marketing, IoT, and other markets at an enterprise scale.
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...
Introduction to Vespa – The Open Source Big Data Serving Engine, Jon Bratseth...Yahoo Developer Network
Offline and stream processing of big data sets can be done with tools such as Hadoop, Spark, and Storm, but what if you need to process big data at the time a user is making a request?
This presentation introduces Vespa (http://vespa.ai) – the open source big data serving engine.
Vespa allows you to search, organize, and evaluate machine-learned models from e.g TensorFlow over large, evolving data sets with latencies in the tens of milliseconds. Vespa is behind the recommendation, ad targeting, and search at Yahoo where it handles billions of daily queries over billions of documents and was recently open sourced at http://vespa.ai.Jun 2017 HUG: YARN Scheduling – A Step Beyond
In recent times, YARN Capacity Scheduler has improved a lot in terms of some critical features and refactoring. Here is a quick look into some of the recent changes in scheduler:
Global Scheduling Support
General placement support
Better preemption model to handle resource anomalies across and within queue.
Absolute resources’ configuration support
Priority support between Queues and Applications
In this talk, we will deep dive into each of these new features to give a better picture of their usage and performance comparison. We will also provide some more brief overview about the ongoing efforts and how they can help to solve some of the core issues we face today.
Speakers:
Sunil Govind (Hortonworks), Jian He (Hortonworks)
Jun 2017 HUG: Large-Scale Machine Learning: Use Cases and Technologies
In recent years, Yahoo has brought the big data ecosystem and machine learning together to discover mathematical models for search ranking, online advertising, content recommendation, and mobile applications. We use distributed computing clusters with CPUs and GPUs to train these models from 100’s of petabytes of data.
A collection of distributed algorithms have been developed to achieve 10-1000x the scale and speed of alternative solutions. Our algorithms construct regression/classification models and semantic vectors within hours, even for billions of training examples and parameters. We have made our distributed deep learning solutions, CaffeOnSpark and TensorFlowOnSpark, available as open source.
In this talk, we highlight Yahoo use cases where big data and machine learning technologies are best exemplified. We explain algorithm/system challenges to scale ML algorithms for massive datasets. We provide a technical overview of CaffeOnSpark and TensorFlowOnSpark to jumpstart your journey of large-scale machine learning.
Speakers:
Andy Feng is a VP of Architecture at Yahoo, leading the architecture and design of big data and machine learning initiatives. He has architected large-scale systems for personalization, ad serving, NoSQL, and cloud infrastructure. Prior to Yahoo, he was a Chief Architect at Netscape/AOL, and Principal Scientist at Xerox. He received a Ph.D. degree in computer science from Osaka University, Japan.
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...
February 2017 HUG: Slow, Stuck, or Runaway Apps? Learn How to Quickly Fix Pro...Yahoo Developer Network
This document discusses the challenges of operationalizing big data applications and how full stack performance intelligence can help DataOps teams address issues. It describes how intelligence can provide automated diagnosis and remediation to solve problems, automated detection and prevention to be proactive, and automated what-if analysis and planning to prepare for future use. Real-life examples show how intelligence can help with proactively detecting SLA violations, diagnosing Hive/Spark application failures, and planning a migration of applications to the cloud.February 2017 HUG: Exactly-once end-to-end processing with Apache Apex
Apache Apex (http://apex.apache.org/) is a stream processing platform that helps organizations to build processing pipelines with fault tolerance and strong processing guarantees. It was built to support low processing latency, high throughput, scalability, interoperability, high availability and security. The platform comes with Malhar library - an extensive collection of processing operators and a wide range of input and output connectors for out-of-the-box integration with an existing infrastructure. In the talk I am going to describe how connectors together with the distributed checkpointing (a mechanism used by the Apex to support fault tolerance and high availability) provide exactly-once end-to-end processing guarantees.
Speakers:
Vlad Rozov is Apache Apex PMC member and back-end engineer at DataTorrent where he focuses on the buffer server, Apex platform network layer, benchmarks and optimizing the core components for low latency and high throughput. Prior to DataTorrent Vlad worked on distributed BI platform at Huawei and on multi-dimensional database (OLAP) at Hyperion Solutions and Oracle.
February 2017 HUG: Data Sketches: A required toolkit for Big Data Analytics
1. Sketch algorithms provide approximate query results with sub-linear space and processing time, enabling analysis of big data that would otherwise require prohibitive resources.
2. Case studies show sketches reduce storage by over 90% and processing time by over 95% compared to exact algorithms, enabling real-time querying and rollups across multiple dimensions that were previously infeasible.
3. The DataSketches library provides open-source implementations of popular sketch algorithms like Theta, HLL, and quantiles sketches, with code samples and adapters for systems like Hive, Pig, and Druid.
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