Quickly find solutions to common programming problems encountered while processing big data. Content is presented in the popular problem-solution format. Look up the programming problem that you want to solve. Read the solution. Apply the solution directly in your own code. Problem solved! PySpark Recipes covers Hadoop and its shortcomings. The architecture of Spark, PySpark, and RDD are presented. You will learn to apply RDD to solve day-to-day big data problems. Python and NumPy are included and make it easy for new learners of PySpark to understand and adopt the model. What You Will Learn 
<ul>
<li>Understand the advanced features of PySpark2 and SparkSQL 
</li>
<li>Optimize your code 
</li>
<li>Program SparkSQL with Python 
</li>
<li>Use Spark Streaming and Spark MLlib with Python 
</li>
<li>Perform graph analysis with GraphFrames 
</li>
</ul>
Who This Book Is ForData analysts, Python programmers, big data enthusiasts

PySpark Recipes: A Problem-Solution Approach with PySpark2

Computers & Technology

Mishra, Raju Kumar

Computer Science

Apress

Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.\nPySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You’ll also discover how to solve problems in graph analysis using graphframes.\nOn completing this book, you’ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.\nWhat You Will Learn\nUnderstand PySpark SQL and its advanced features 
Use SQL and HiveQL with PySpark SQL 
Work with structured streaming 
Optimize PySpark SQL 
Master graphframes and graph processing\nWho This Book Is For\nData scientists, Python programmers, and SQL programmers.\nFrom the Back Cover\nCarry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides solutions to problems related to dataframes, data manipulation summarization, and exploratory analysis. You will improve your skills in graph data analysis using graphframes and see how to optimize your PySpark SQL code.\nPySpark SQL Recipes starts with recipes on creating dataframes from different types of data source, data aggregation and summarization, and exploratory data analysis using PySpark SQL. You’ll also discover how to solve problems in graph analysis using graphframes.\nOn completing this book, you’ll have ready-made code for all your PySpark SQL tasks, including creating dataframes using data from different file formats as well as from SQL or NoSQL databases.\nYou will:\nUnderstand PySpark SQL and its advanced features 
Use SQL and HiveQL with PySpark SQL 
Work with structured streaming 
Optimize PySpark SQL 
Master graphframes and graph processing

PySpark SQL Recipes: With HiveQL, Dataframe and Graphframes

Raman, Sundar Rajan

Operating Systems

See the best price to sell, buy, or rent books by Mishra, Raju Kumar. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Mishra, Raju Kumar