Bøger af Ted Dunning

Building a simple but powerful recommendation system is much easier than you think. Approachable for all levels of expertise, this report explains innovations that make machine learning practical for business production settingsand demonstrates how even a small-scale development team can design an effective large-scale recommendation system.Apache Mahout committers Ted Dunning and Ellen Friedman walk you through a design that relies on careful simplification. Youll learn how to collect the right data, analyze it with an algorithm from the Mahout library, and then easily deploy the recommender using search technology, such as Apache Solr or Elasticsearch. Powerful and effective, this efficient combination does learning offline and delivers rapid response recommendations in real time.Understand the tradeoffs between simple and complex recommendersCollect user data that tracks user actionsrather than their ratingsPredict what a user wants based on behavior by others, using Mahoutfor co-occurrence analysisUse search technology to offer recommendations in real time, complete with item metadataWatch the recommender in action with a music service exampleImprove your recommender with dithering, multimodal recommendation, and other techniques

More and more data-driven companies are looking to adopt stream processing and streaming analytics. With this concise ebook, youll learn best practices for designing a reliable architecture that supports this emerging big-data paradigm.Authors Ted Dunning and Ellen Friedman (Real World Hadoop) help you explore some of the best technologies to handle stream processing and analytics, with a focus on the upstream queuing or message-passing layer. To illustrate the effectiveness of these technologies, this book also includes specific use cases.Ideal for developers and non-technical people alike, this book describes:Key elements in good design for streaming analytics, focusing on the essential characteristics of the messaging layerNew messaging technologies, including Apache Kafka and MapR Streams, with links to sample codeTechnology choices for streaming analytics: Apache Spark Streaming, Apache Flink, Apache Storm, and Apache ApexHow stream-based architectures are helpful to support microservicesSpecific use cases such as fraud detection and geo-distributed data streamsTed Dunning is Chief Applications Architect at MapR Technologies, and active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and as committer and PMC member of the Apache ZooKeeper and Drill projects. Ted is on Twitter as @ted_dunning.Ellen Friedman, a committer for the Apache Drill and Apache Mahout projects, is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. With a PhD in Biochemistry, she has years of experience as a research scientist and has written about a variety of technical topics. Ellen is on Twitter as @Ellen_Friedman.

Many big data-driven companies today are moving to protect certain types of data against intrusion, leaks, or unauthorized eyes. But how do you lock down data while granting access to people who need to see it? In this practical book, authors Ted Dunning and Ellen Friedman offer two novel and practical solutions that you can implement right away.Ideal for both technical and non-technical decision makers, group leaders, developers, and data scientists, this book shows you how to:Share original data in a controlled way so that different groups within your organization only see part of the whole. Youll learn how to do this with the new open source SQL query engine Apache Drill.Provide synthetic data that emulates the behavior of sensitive data. This approach enables external advisors to work with you on projects involving data that you can't show them.If youre intrigued by the synthetic data solution, explore the log-synth program that Ted Dunning developed as open source code (available on GitHub), along with how-to instructions and tips for best practice. Youll also get a collection of use cases.Providing lock-down security while safely sharing data is a significant challenge for a growing number of organizations. With this book, youll discover new options to share data safely without sacrificing security.

If youre a business team leader, CIO, business analyst, or developer interested in how Apache Hadoop and Apache HBase-related technologies can address problems involving large-scale data in cost-effective ways, this book is for you. Using real-world stories and situations, authors Ted Dunning and Ellen Friedman show Hadoop newcomers and seasoned users alike how NoSQL databases and Hadoop can solve a variety of business and research issues.Youll learn about early decisions and pre-planning that can make the process easier and more productive. If youre already using these technologies, youll discover ways to gain the full range of benefits possible with Hadoop. While you dont need a deep technical background to get started, this book does provide expert guidance to help managers, architects, and practitioners succeed with their Hadoop projects.Examine a day in the life of big data: Indias ambitious Aadhaar projectReview tools in the Hadoop ecosystem such as Apaches Spark, Storm, and Drill to learn how they can help youPick up a collection of technical and strategic tips that have helped others succeed with HadoopLearn from several prototypical Hadoop use cases, based on how organizations have actually applied the technologyExplore real-world stories that reveal how MapR customers combine use cases when putting Hadoop and NoSQL to work, including in production

This O'Reilly report uses practical example to explain how the underlying concepts of anomaly detection work.

Time series data is of growing importance, especially with the rapid expansion of the Internet of Things. This concise guide shows you effective ways to collect, persist, and access large-scale time series data for analysis.