This book will help you work on prototypes on local machines and subsequently go on to handle messy data in production and at scale. 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. 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. One of the many uses of Apache Spark is for data analytics applications across clustered computers. ![]() ![]() Apache Spark is an open source parallel-processing framework that has been around for quite some time now.
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