Ultimate Big Data Analytics with Apache Hadoop

Master the Hadoop Ecosystem and Build Scalable Analytics Systems

Key Features ? Explains Hadoop, YARN, MapReduce, and Tez for understanding distributed data processing and resource management. ? Delves into Apache Hive and Apache Spark for their roles in data warehousing, real-time processing, and advanced analytics. ? Provides hands-on guidance for using Python with Hadoop for business intelligence and data analytics.

Book Description In a rapidly evolving Big Data job market projected to grow by 28% through 2026 and with salaries reaching up to $150,000 annually—mastering big data analytics with the Hadoop ecosystem is most sought after for career advancement. The Ultimate Big Data Analytics with Apache Hadoop is an indispensable companion offering in-depth knowledge and practical skills needed to excel in today's data-driven landscape.

The book begins laying a strong foundation with an overview of data lakes, data warehouses, and related concepts. It then delves into core Hadoop components such as HDFS, YARN, MapReduce, and Apache Tez, offering a blend of theory and practical exercises.

You will gain hands-on experience with query engines like Apache Hive and Apache Spark, as well as file and table formats such as ORC, Parquet, Avro, Iceberg, Hudi, and Delta. Detailed instructions on installing and configuring clusters with Docker are included, along with big data visualization and statistical analysis using Python.

Given the growing importance of scalable data pipelines, this book equips data engineers, analysts, and big data professionals with practical skills to set up, manage, and optimize data pipelines, and to apply machine learning techniques effectively.

Don't miss out on the opportunity to become a leader in the big data field to unlock the full potential of big data analytics with Hadoop.

What you will learn ? Gain expertise in building and managing large-scale data pipelines with Hadoop, YARN, and MapReduce. ? Master real-time analytics and data processing with Apache Spark's powerful features. ? Develop skills in using Apache Hive for efficient data warehousing and complex queries. ? Integrate Python for advanced data analysis, visualization, and business intelligence in the Hadoop ecosystem. ? Learn to enhance data storage and processing performance using formats like ORC, Parquet, and Delta. ? Acquire hands-on experience in deploying and managing Hadoop clusters with Docker and Kubernetes. ? Build and deploy machine learning models with tools integrated into the Hadoop ecosystem.

Table of Contents 1. Introduction to Hadoop and ASF 2. Overview of Big Data Analytics 3. Hadoop and YARN MapReduce and Tez 4. Distributed Query Engines: Apache Hive 5. Distributed Query Engines: Apache Spark 6. File Formats and Table Formats (Apache Ice-berg, Hudi, and Delta) 7. Python and the Hadoop Ecosystem for Big Data Analytics - BI 8. Data Science and Machine Learning with Hadoop Ecosystem 9. Introduction to Cloud Computing and Other Apache Projects Index

About the Authors Simhadri Govindappa holds a Bachelor of Engineering in Electronics and Communication Engineering from M.S. Ramaiah Institute of Technology, Bangalore, India. He is an accomplished professional with significant contributions to the field of big data.

Simhadri began his career at GE Healthcare as part of the AI data platform team, where he developed AI models and deep learning annotation tools. His work led to a patent granted by the USPTO (patent no: US11069036B1). He then moved to Cloudera, a pioneer in big data, joining the Apache Hive R&D team. His work primarily focuses on Distributed systems, Apache Iceberg, Apache Hive, Hive- ACID-Spark Connectivity (HWC), and enhancing Hive Acid functionality.