{"product_id":"data-algorithms-with-spark-recipes-and-design-patterns-for-scaling-up-using-pyspark-9781492082385","title":"Data Algorithms with Spark: Recipes and Design Patterns for Scaling Up Using Pyspark","description":"\u003cp\u003eApache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. \u003c\/p\u003e\u003cp\u003e In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. \u003c\/p\u003e\u003cp\u003e With this book, you will: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eLearn how to select Spark transformations for optimized solutions \u003c\/li\u003e\n\u003cli\u003eExplore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() \u003c\/li\u003e\n\u003cli\u003eUnderstand data partitioning for optimized queries \u003c\/li\u003e\n\u003cli\u003eBuild and apply a model using PySpark design patterns \u003c\/li\u003e\n\u003cli\u003eApply motif-finding algorithms to graph data \u003c\/li\u003e\n\u003cli\u003eAnalyze graph data by using the GraphFrames API \u003c\/li\u003e\n\u003cli\u003eApply PySpark algorithms to clinical and genomics data \u003c\/li\u003e\n\u003cli\u003eLearn how to use and apply feature engineering in ML algorithms \u003c\/li\u003e\n\u003cli\u003eUnderstand and use practical and pragmatic data design patterns \u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eMahmoud Parsian, Ph.D. in Computer Science, is a practicing software professional with 30 years of experience as a developer, designer, architect, and author. For the past 15 years, he has been involved in Java server-side, databases, MapReduce, Spark, PySpark, and distributed computing. Dr. Parsian currently leads Illumina's Big Data team, which is focused on large-scale genome analytics and distributed computing by using Spark and PySpark. He leads and develops scalable regression algorithms; DNA sequencing pipelines using Java, MapReduce, PySpark, Spark, and open source tools. He is the author of the following books: Data Algorithms (O'Reilly, 2015), PySpark Algorithms (Amazon.com, 2019), JDBC Recipes (Apress, 2005), JDBC Metadata Recipes (Apress, 2006). Also, Dr. Parsian is an Adjunct Professor at Santa Clara University, teaching Big Data Modeling and Analytics and Machine Learning to MSIS program utilizing Spark, PySpark, Python, and scikit-learn.\u003c\/p\u003e\u003cbr\u003e","brand":"O'Reilly Media","offers":[{"title":"Default Title","offer_id":50495898485010,"sku":"9781492082385","price":57.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_b2b27e57-960d-431e-8567-f8bbd3f8fbbc.jpg?v=1730688866","url":"https:\/\/surprise-castle.myshopify.com\/products\/data-algorithms-with-spark-recipes-and-design-patterns-for-scaling-up-using-pyspark-9781492082385","provider":"Surprise Castle","version":"1.0","type":"link"}