{"product_id":"9780134435510","title":"Data Munging with Hadoop","description":"\u003cb\u003eThe Example-Rich, Hands-On Guide to Data Munging with Apache Hadoop\u003csup\u003eTM\u003c\/sup\u003e \u003c\/b\u003e   \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eData scientists spend much of their time “munging” data: handling day-to-day tasks such as data cleansing, normalization, aggregation, sampling, and transformation. These tasks are both critical and surprisingly interesting. Most important, they deepen your understanding of your data’s structure and limitations: crucial insight for improving accuracy and mitigating risk in any analytical project.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eNow, two leading Hortonworks data scientists, Ofer Mendelevitch and Casey Stella, bring together powerful, practical insights for effective Hadoop-based data munging of large datasets. Drawing on extensive experience with advanced analytics, the authors offer realistic examples that address the common issues you’re most likely to face. They describe each task in detail, presenting example code based on widely used tools such as Pig, Hive, and Spark.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eThis concise, hands-on eBook is valuable for every data scientist, data engineer, and architect who wants to master data munging: not just in theory, but in practice with the field’s #1 platform–Hadoop.\u003c\/p\u003e  \u003cp\u003e \u003c\/p\u003e  \u003cp\u003eCoverage includes\u003c\/p\u003e  \u003cul\u003e  \u003cli\u003eA framework for understanding the various types of data quality checks, including cell-based rules, distribution validation, and outlier analysis   \u003c\/li\u003e\n\u003cli\u003eAssessing tradeoffs in common approaches to imputing missing values   \u003c\/li\u003e\n\u003cli\u003eImplementing quality checks with Pig or Hive UDFs   \u003c\/li\u003e\n\u003cli\u003eTransforming raw data into “feature matrix” format for machine learning algorithms   \u003c\/li\u003e\n\u003cli\u003eChoosing features and instances   \u003c\/li\u003e\n\u003cli\u003eImplementing text features via “bag-of-words” and NLP techniques   \u003c\/li\u003e\n\u003cli\u003eHandling time-series data via frequency- or time-domain methods   \u003c\/li\u003e\n\u003cli\u003eManipulating feature values to prepare for modeling\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003e \u003ci\u003eData Munging with Hadoop \u003c\/i\u003eis part of a larger, forthcoming work entitled \u003ci\u003eData Science \u003c\/i\u003e \u003ci\u003eUsing Hadoop\u003c\/i\u003e. To be notified when the larger work is available, register your purchase of \u003ci\u003eData Munging with Hadoop \u003c\/i\u003eat informit.com\/register and check the box “I would like to hear from InformIT and its family of brands about products and special offers.”\u003c\/p\u003e","brand":"Pearson Education","offers":[{"title":"Default Title","offer_id":47080406089968,"sku":"9780134435510","price":9.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/7593\/9824\/files\/9780134435510_p0.jpg?v=1763641458","url":"https:\/\/shop-qa.barnesandnoble.com\/products\/9780134435510","provider":"Barnes \u0026 Noble (DEV)","version":"1.0","type":"link"}