Hands-On Big Data Analytics with PySpark - Helion
Tytuł oryginału: Hands-On Big Data Analytics with PySpark
ISBN: 978-18-386-4883-1
Format: ebook
Data wydania: 2019-03-29
Księgarnia: Helion
Cena książki: 84,99 zł
Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable Spark jobs
Key Features
- Work with large amounts of agile data using distributed datasets and in-memory caching
- Source data from all popular data hosting platforms, such as HDFS, Hive, JSON, and S3
- Employ the easy-to-use PySpark API to deploy big data Analytics for production
Book Description
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.
What you will learn
- Get practical big data experience while working on messy datasets
- Analyze patterns with Spark SQL to improve your business intelligence
- Use PySpark's interactive shell to speed up development time
- Create highly concurrent Spark programs by leveraging immutability
- Discover ways to avoid the most expensive operation in the Spark API: the shuffle operation
- Re-design your jobs to use reduceByKey instead of groupBy
- Create robust processing pipelines by testing Apache Spark jobs
Who this book is for
This book is for developers, data scientists, business analysts, or anyone who needs to reliably analyze large amounts of large-scale, real-world data. Whether you're tasked with creating your company's business intelligence function or creating great data platforms for your machine learning models, or are looking to use code to magnify the impact of your business, this book is for you.
Osoby które kupowały "Hands-On Big Data Analytics with PySpark", wybierały także:
- Scala for Machine Learning - Second Edition 186,88 zł, (29,90 zł -84%)
- Data Analysis with IBM SPSS Statistics 186,88 zł, (29,90 zł -84%)
- QlikView for Developers 186,88 zł, (29,90 zł -84%)
- Practical Machine Learning Cookbook 186,88 zł, (29,90 zł -84%)
- Mastering Hadoop 3 175,88 zł, (29,90 zł -83%)
Spis treści
Hands-On Big Data Analytics with PySpark. Analyze large datasets and discover techniques for testing, immunizing, and parallelizing Spark jobs eBook -- spis treści
- 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