Dive into the world of scalable data processing with 'Essential PySpark for Scalable Data Analytics'. This book is a comprehensive guide that helps beginners understand and utilize PySpark to process, analyze, and draw insights from large datasets effectively. With hands-on tutorials and clear explanations, you will gain the confidence to tackle big data analytics challenges. What this Book will help me do Understand and apply the distributed computing paradigm for big data. Learn to perform scalable data ingestion, cleansing, and preparation using PySpark. Create and utilize data lakes and the Lakehouse paradigm for efficient data storage and access. Develop and deploy machine learning models with scalability in mind. Master real-time analytics pipelines and create impactful data visualizations. Author(s) None Nudurupati is an experienced data engineer and educator, specializing in distributed systems and big data technologies. With years of practical experience in the field, None brings a clear and approachable teaching style to technical topics. Passionate about empowering readers, the author has designed this book to be both practical and inspirational for aspiring data practitioners. Who is it for? This book is ideal for data professionals including data scientists, engineers, and analysts looking to scale their data analytics processes. It assumes familiarity with basic data science concepts and Python, as well as some experience with SQL-like data analysis. This is particularly suitable for individuals aiming to expand their knowledge in distributed computing and PySpark to handle big data challenges. Achieving scalable and efficient data solutions is at the core of this guide.
