Essential PySpark for Data Analytics

Now the de facto standard for big data processing, Apache Spark is a unified analytics engine that provides distributed computing framework across multiple computers to process large volumes of data in a fast and efficient way. A part of Apache Spark, PySpark is a Python language API that offers an easy-to-use interface for developers working on data analytics.

Essential PySpark for Data Analytics starts by exploring the distributed computing paradigm and provides a high-level overview of how Apache Spark helps simplify distributed computing. You’ll then begin your data analytics journey with data engineering, ETL, and data cleansing. You’ll learn how to use data science and machine learning to perform predictive analytics, use data analysis to gain insights from processed data and predictive models, and communicate results to business users via data visualization tools. With the help of this PySpark book, you’ll also discover how real-time analytics enable you to gain insights much faster, explore an alternative API on Spark called Koalas, and briefly touch upon techniques for scaling machine learning using PySpark.

By the end of this book, you’ll have gained a solid understanding of distributed computing and how to harness the power of PySpark for data engineering, data science, and data analysis to solve business problems.

Book Features:

• Discover how to convert huge amounts of raw data into meaningful and actionable insights
• Use Spark’s unified analytics engine for end-to-end analytics, from data preparation to predictive analytics
• Explore PySpark for data cleansing, analysis, integration, ML, querying, and real-time analytics