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Hands-On Big Data Analytics with PySpark. Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

Hands-On Big Data Analytics with PySpark. Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs

James Cross, Rudy Lai, Bartłomiej Potaczek

E-book
Apache Spark is an open source parallel-processing framework that has been around for quite some time now. One of the many uses of Apache Spark is for data analytics applications across clustered computers. In this book, you will not only learn how to use Spark and the Python API to create high-performance analytics with big data, but also discover techniques for testing, immunizing, and parallelizing Spark jobs.
You will learn how to source data from all popular data hosting platforms, including HDFS, Hive, JSON, and S3, and deal with large datasets with PySpark to gain practical big data experience. This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. This book covers installing and setting up PySpark, RDD operations, big data cleaning and wrangling, and aggregating and summarizing data into useful reports. You will also learn how to implement some practical and proven techniques to improve certain aspects of programming and administration in Apache Spark.
By the end of the book, you will be able to build big data analytical solutions using the various PySpark offerings and also optimize them effectively.
  • 1. Installing Pyspark and Setting up Your Development Environment
  • 2. Getting Your Big Data into the Spark Environment Using RDDs
  • 3. Big Data Cleaning and Wrangling with Spark Notebooks
  • 4. Aggregating and Summarizing Data into Useful Reports
  • 5. Powerful Exploratory Data Analysis with MLlib
  • 6. Putting Structure on Your Big Data with SparkSQL
  • 7. Transformations and Actions
  • 8. Immutable Design
  • 9. Avoiding Shuffle and Reducing Operational Expenses
  • 10. Saving Data in the Correct Format
  • 11. Working with the Spark Key/Value API
  • 12. Testing Apache Spark Jobs
  • 13. Leveraging the Spark GraphX API
  • Titel: Hands-On Big Data Analytics with PySpark. Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs
  • Autor: James Cross, Rudy Lai, Bartłomiej Potaczek
  • Originaler Titel: Hands-On Big Data Analytics with PySpark. Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs
  • ISBN: 9781838648831, 9781838648831
  • Veröffentlichungsdatum: 2019-03-29
  • Format: E-book
  • Artikelkennung: e_14t5
  • Verleger: Packt Publishing