Book Description
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
- Understand the advanced features of PySpark2 and SparkSQL
- Optimize your code
- Program SparkSQL with Python
- Use Spark Streaming and Spark MLlib with Python
- Perform graph analysis with GraphFrames
Who This Book Is For
Data analysts, Python programmers, big data enthusiasts
Table of Contents
- Cover
- Frontmatter
- 1. The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks
- 2. Installation
- 3. Introduction to Python and NumPy
- 4. Spark Architecture and the Resilient Distributed Dataset
- 5. The Power of Pairs: Paired RDDs
- 6. I/O in PySpark
- 7. Optimizing PySpark and PySpark Streaming
- 8. PySparkSQL
- 9. PySpark MLlib and Linear Regression
- Backmatter