Building analytics products at scale requires a deep investment in people, machines, and time. How can you be sure you’re building the right models that people will pay for? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Spark.Using lightweight tools such as Python, PySpark, Elastic MapReduce, MongoDB, ElasticSearch, Doc2vec, Deep Learning, D3.js, Leaflet, Docker and Heroku, your team will create an agile environment for exploring data, starting with an example application to mine flight data into an analytic product. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working applications.Create analytics applications by using the Agile Data Science development methodologyBuild value from your data in a series of agile sprints, using the data-value pyramidLearn how to build and deploy predictive analytics using Kafka and Spark StreamingExtract features for statistical models from a single datasetVisualize data with charts, and expose different aspects through interactive reportsUse historical data to predict the future via classification and regressionTranslate predictions into actionsGet feedback from users after each sprint to keep your project on track

Book Description

Building Full-Stack Data Analytics Applications with Spark

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About the Author

Russell Jurney cut his data teeth in casino gaming, building web apps to analyze the performance of slot machines in the US and Mexico. After dabbling in entrepreneurship, interactive media and journalism, he moved to silicon valley to build analytics applications at scale at Ning and LinkedIn. He lives on the ocean in Pacifica, California with his wife Kate and two fuzzy dogs.

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