{ "item_title" : "Data Analysis with Python and Pyspark", "item_author" : [" Jonathan Rioux "], "item_description" : "Think big about your data PySpark brings the powerful Spark big data processing engine to the Python ecosystem, letting you seamlessly scale up your data tasks and create lightning-fast pipelines. In Data Analysis with Python and PySpark you will learn how to: Manage your data as it scales across multiple machinesScale up your data programs with full confidenceRead and write data to and from a variety of sources and formatsDeal with messy data with PySpark's data manipulation functionalityDiscover new data sets and perform exploratory data analysisBuild automated data pipelines that transform, summarize, and get insights from dataTroubleshoot common PySpark errorsCreating reliable long-running jobs Data Analysis with Python and PySpark is your guide to delivering successful Python-driven data projects. Packed with relevant examples and essential techniques, this practical book teaches you to build pipelines for reporting, machine learning, and other data-centric tasks. Quick exercises in every chapter help you practice what you've learned, and rapidly start implementing PySpark into your data systems. No previous knowledge of Spark is required. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technologyThe Spark data processing engine is an amazing analytics factory: raw data comes in, insight comes out. PySpark wraps Spark's core engine with a Python-based API. It helps simplify Spark's steep learning curve and makes this powerful tool available to anyone working in the Python data ecosystem. About the bookData Analysis with Python and PySpark helps you solve the daily challenges of data science with PySpark. You'll learn how to scale your processing capabilities across multiple machines while ingesting data from any source--whether that's Hadoop clusters, cloud data storage, or local data files. Once you've covered the fundamentals, you'll explore the full versatility of PySpark by building machine learning pipelines, and blending Python, pandas, and PySpark code. What's inside Organizing your PySpark codeManaging your data, no matter the sizeScale up your data programs with full confidenceTroubleshooting common data pipeline problemsCreating reliable long-running jobs About the readerWritten for data scientists and data engineers comfortable with Python. About the authorAs a ML director for a data-driven software company, Jonathan Rioux uses PySpark daily. He teaches the software to data scientists, engineers, and data-savvy business analysts. Table of Contents 1 IntroductionPART 1 GET ACQUAINTED: FIRST STEPS IN PYSPARK2 Your first data program in PySpark3 Submitting and scaling your first PySpark program4 Analyzing tabular data with pyspark.sql5 Data frame gymnastics: Joining and groupingPART 2 GET PROFICIENT: TRANSLATE YOUR IDEAS INTO CODE6 Multidimensional data frames: Using PySpark with JSON data7 Bilingual PySpark: Blending Python and SQL code8 Extending PySpark with Python: RDD and UDFs9 Big data is just a lot of small data: Using pandas UDFs10 Your data under a different lens: Window functions11 Faster PySpark: Understanding Spark's query planningPART 3 GET CONFIDENT: USING MACHINE LEARNING WITH PYSPARK12 Setting the stage: Preparing features for machine learning13 Robust machine learning with ML Pipelines14 Building custom ML transformers and estimators", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/1/61/729/720/1617297208_b.jpg", "price_data" : { "retail_price" : "59.99", "online_price" : "59.99", "our_price" : "59.99", "club_price" : "59.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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