Shikha Mehta
Technologies like Hadoop and Spark, powered by the Cloudera platform, have become essential for storing, processing, and analyzing big data across various industries, including finance, healthcare, e-commerce, and research in today’s data-driven world.This book systematically navigates the entire ecosystem, starting with big data fundamentals, security, and HDFS architecture before mastering MapReduce through weather and stock data case studies. Readers will gain hands-on experience with the Cloudera framework, learning high-level scripting with Pig Latin and structured data warehousing using HiveQL’s Metastore and partitions. Additionally, it explores NoSQL versatility with HBase and MongoDB’s CAP theorem, followed by Scala programming and Spark’s high-speed in-memory engine. You will learn to optimize queries with the Catalyst optimizer and process complex Parquet or JSON files using Spark SQL DataFrames. The book also covers machine learning pipelines with spark.ml for professional-grade classification and clustering applications.By the end of this book, readers will be able to develop strong conceptual clarity and practical expertise in big data analytics. This will enable them to confidently design, implement, and manage scalable data processing solutions, preparing them to solve real-world data challenges and take on professional roles in big data engineering and analytics.WHAT YOU WILL LEARN● Understand big data concepts, architecture, ethics, and applications.● Build scalable storage using HDFS and MapReduce.● Perform data analysis using Pig and Hive.● Develop NoSQL solutions using HBase and MongoDB.● Process large datasets using Apache Spark.● Analyze data using Spark SQL and DataFrames.● Implement machine learning using PySpark.WHO THIS BOOK IS FORThis book is ideal for students, researchers, and academicians. It empowers aspiring big data engineers, data scientists, and software engineers. Readers should possess basic programming knowledge and database fundamentals to master Hadoop and Spark for professional-grade data science and faculty-level instruction.
