PySpark helps you perform data analysis at-scale; it enables you to build more scalable analyses and pipelines. This course starts by introducing you to PySpark's potential for performing effective analyses of large datasets. You'll learn how to interact with Spark from Python and connect Jupyter to Spark to provide rich data visualizations. After that, you'll delve into various Spark components and its architecture. You'll learn to work with Apache Spark and perform ML tasks more smoothly than before. Gathering and querying data using Spark SQL, to overcome challenges involved in reading it. You'll use the DataFrame API to operate with Spark MLlib and learn about the Pipeline API. Finally, we provide tips and tricks for deploying your code and performance tuning. By the end of this course, you will not only be able to perform efficient data analytics but will have also learned to use PySpark to easily analyze large datasets at-scale in your organization. All related code files are placed on a GitHub repository at: https://github.com/PacktPublishing/Ma.... This course will greatly appeal to data science enthusiasts, data scientists, or anyone who is familiar with Machine Learning concepts and wants to scale out his/her work to work with big data. If you find it difficult to analyze large datasets that keep growing, then this course is the perfect guide for you! A working knowledge of Python assumed.
