PySpark Recipes

Presents advanced features of PySpark and code optimization techniques

Covers SparkSQL, Spark Streaming, Spark MLlib, and GraphFrames
Discusses and demonstrates Data Science and Big Data processing with PySpark MLlib

Presents advanced features of PySpark and code optimization techniques Covers SparkSQL, Spark Streaming, Spark MLlib, and GraphFrames Discusses and demonstrates Data Science and Big Data processing with PySpark MLlib

Autorentext
Raju Mishra has strong interests in data science and systems that have the capability of handling large amounts of data and operating complex mathematical models through computational programming. He was inspired to pursue an M. Tech in computational sciences from Indian Institute of Science in Bangalore, India. Raju primarily works in the areas of data science and its different applications. Working as a corporate trainer he has developed unique insights that help him in teaching and explaining complex ideas with ease. Raju is also a data science consultant solving complex industrial problems. He works on programming tools such as R, Python, scikit-learn, Statsmodels, Hadoop, Hive, Pig, Spark, and many others.

Klappentext
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 PySpark and SparkSQL

• Optimize your code

• Program SparkSQL with Python

• Use Spark Streaming and Spark MLlib with Python

• Perform graph analysis with GraphFrames

  Inhalt
  Chapter 1: The Era of Big Data, Hadoop, and Other Big Data Processing Frameworks.- Chapter 2: Installation.- Chapter 3: Introduction to Python and NumPy.- Chapter 4: Spark Architecture and Resilient Distributed Dataset.- Chapter 5: The Power of Pairs: Paired RDD.- Chapter 6: IO in PySpark.- Chapter 7: Optimizing PySpark and PySpark Streaming.- Chapter 8: PySparkSQL.- Chapter 9: PySpark MLlib and Linear Regression.